Search results for: Kazakh speech dataset

Authors: Sanja Seljan, Ivan Dunđer

Abstract:

The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

Procedia PDF Downloads 461

Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

Abstract:

Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

Procedia PDF Downloads 67

Authors: Naouel Zoghlami

Abstract:

Spoken-word recognition involves the simultaneous activation of potential word candidates which compete with each other for final correct recognition. In continuous speech, the activation-competition process gets more complicated due to speech reductions existing at word boundaries. Lexical processing is more difficult in L2 than in L1 because L2 listeners often lack phonetic, lexico-semantic, syntactic, and prosodic knowledge in the target language. In this study, we investigate the on-line lexical segmentation hypotheses that French listeners of L2 English form and then revise as subsequent perceptual evidence is revealed. Our purpose is to shed further light on the processes of L2 spoken-word recognition in context and better understand L2 listening difficulties through a comparison of skilled and unskilled reactions at the point where their working hypothesis is rejected. We use a variant of the gating experiment in which subjects transcribe an English sentence presented in increments of progressively greater duration. The spoken sentence was “And this amazing athlete has just broken another world record”, chosen mainly because it included common reductions and phonetic features in English, such as elision and assimilation. Our preliminary results show that there is an important difference in the manner in which proficient and less-proficient L2 listeners handle connected speech. Less-proficient listeners delay recognition of words as they wait for lexical and syntactic evidence to appear in the gates. Further statistical results are currently being undertaken.

Keywords: gating paradigm, spoken word recognition, online lexical segmentation, L2 listening

Procedia PDF Downloads 445

Authors: Heeba A. Gurku

Abstract:

Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

Procedia PDF Downloads 63

Authors: Arianna Vedaschi

Abstract:

This paper addresses the relationship between freedom of expression, national security and radicalization. Is it still possible to talk about a balance between the first two elements? Or, due to the intrusion of the third, is it more appropriate to consider freedom of expression as “permanently disfigured” by securitarian concerns? In this study, both the legislative and the judicial level are taken into account and the comparative method is employed in order to provide the reader with a complete framework of relevant issues and a workable set of solutions. The analysis moves from the finding according to which the tension between free speech and national security has become a major issue in democratic countries, whose very essence is continuously endangered by the ever-changing and multi-faceted threat of international terrorism. In particular, a change in terrorist groups’ recruiting pattern, attracting more and more people by way of a cutting-edge communicative strategy, often employing sophisticated technology as a radicalization tool, has called on law-makers to modify their approach to dangerous speech. While traditional constitutional and criminal law used to punish speech only if it explicitly and directly incited the commission of a criminal action (“cause-effect” model), so-called glorification offences – punishing mere ideological support for terrorism, often on the web – are becoming commonplace in the comparative scenario. Although this is direct, and even somehow understandable, consequence of the impending terrorist menace, this research shows many problematic issues connected to such a preventive approach. First, from a predominantly theoretical point of view, this trend negatively impacts on the already blurred line between permissible and prohibited speech. Second, from a pragmatic point of view, such legislative tools are not always suitable to keep up with ongoing developments of both terrorist groups and their use of technology. In other words, there is a risk that such measures become outdated even before their application. Indeed, it seems hard to still talk about a proper balance: what was previously clearly perceived as a balancing of values (freedom of speech v. public security) has turned, in many cases, into a hierarchy with security at its apex. In light of these findings, this paper concludes that such a complex issue would perhaps be better dealt with through a combination of policies: not only criminalizing ‘terrorist speech,’ which should be relegated to a last resort tool, but acting at an even earlier stage, i.e., trying to prevent dangerous speech itself. This might be done by promoting social cohesion and the inclusion of minorities, so as to reduce the probability of people considering terrorist groups as a “viable option” to deal with the lack of identification within their social contexts.

Keywords: radicalization, free speech, international terrorism, national security

Procedia PDF Downloads 179

Authors: Pushpavathi M., Kavya V., Akshatha V.

Abstract:

Introduction: Cleft lip and palate (CLP) is a congenital condition which hinders effectual communication due to associated speech and language difficulties. Expressive language delay (ELD) is a feature seen in this population which is influenced by factors such as type and severity of CLP, age at surgical and linguistic intervention and also the type and intensity of speech and language therapy (SLT). Since CLP is the most common congenital abnormality seen in Indian children, early intervention is a necessity which plays a critical role in enhancing their speech and language skills. The interaction between the timing of intervention and factors which contribute to effective intervention by caregivers is an area which needs to be explored. Objectives: The present study attempts to determine the effect of timing of intervention on the contributing maternal factors for effective linguistic intervention in toddlers with repaired CLP with respect to the awareness, home training patterns, speech and non-speech behaviors of the mothers. Participants: Thirty six toddlers in the age range of 1 to 4 years diagnosed as ELD secondary to repaired CLP, along with their mothers served as participants. Group I (Early Intervention Group, EIG) included 19 mother-child pairs who came to seek SLT soon after corrective surgery and group II (Delayed Intervention Group, DIG) included 16 mother-child pairs who received SLT after the age of 3 years. Further, the groups were divided into group A, and group B. Group ‘A’ received SLT for 60 sessions by Speech Language Pathologist (SLP), while Group B received SLT for 30 sessions by SLP and 30 sessions only by mother without supervision of SLP. Method: The mothers were enrolled for the Early Language Intervention Program and following this, their awareness about CLP was assessed through the Parental awareness questionnaire. The quality of home training was assessed through Mohite’s Inventory. Subsequently, the speech and non-speech behaviors of the mothers were assessed using a Mother’s behavioral checklist. Detailed counseling and orientation was done to the mothers, and SLT was initiated for toddlers. After 60 sessions of intensive SLT, the questionnaire and checklists were re-administered to find out the changes in scores between the pre- and posttest measurements. Results: The scores obtained under different domains in the awareness questionnaire, Mohite’s inventory and Mothers behavior checklist were tabulated and subjected to statistical analysis. Since the data did not follow normal distribution (i.e. p > 0.05), Mann-Whitney U test was conducted which revealed that there was no significant difference between groups I and II as well as groups A and B. Further, Wilcoxon Signed Rank test revealed that mothers had better awareness regarding issues related to CLP and improved home-training abilities post-orientation (p ≤ 0.05). A statistically significant difference was also noted for speech and non-speech behaviors of the mothers (p ≤ 0.05). Conclusions: Extensive orientation and counseling helped mothers of both EI and DI groups to improve their knowledge about CLP. Intensive SLT using focused stimulation and a parent-implemented approach enabled them to carry out the intervention in an effectual manner.

Keywords: awareness, cleft lip and palate, early language intervention program, home training, orientation, timing of intervention

Procedia PDF Downloads 104

Authors: Swapna N., Deepthy Ann Joy

Abstract:

One of the major causes of communication disorders in pediatric population is Motor speech disorders. These disorders which affect the motor aspects of speech articulators can have an adverse effect on the communication abilities of children in their developmental period. The motor aspects are dependent on the sensory abilities of children with motor speech disorders. Hence, oral sensorimotor evaluation is an important component in the assessment of children with motor speech disorders. To our knowledge, the importance of oral motor examination has been well established, yet the sensory assessment of the oral structures has received less focus. One of the most common motor speech disorders seen in children is developmental dysarthria. The present study aimed to assess the orosensory aspects in children with developmental dysarthria (CDD). The control group consisted of 240 children in the age range of four and eight years which was divided into four subgroups (4-4.11, 5-5.11, 6-6.11 and 7-7.11 years). The experimental group consisted of 15 children who were diagnosed with developmental dysarthria secondary to cerebral palsy who belonged in the age range of four and eight years. The oro-sensory aspects such as response to touch, temperature, taste, texture, and orofacial sensitivity were evaluated and profiled. For this purpose, the authors used the ‘Oral Sensorimotor Evaluation Protocol- Children’ which was developed by the authors. The oro-sensory section of the protocol was administered and the clinical profile of oro-sensory abilities of typically developing children and CDD was obtained for each of the sensory abilities. The oro-sensory abilities of speech articulators such as lips, tongue, palate, jaw, and cheeks were assessed in detail and scored. The results indicated that experimental group had poorer scores on oro-sensory aspects such as light static touch, kinetic touch, deep pressure, vibration and double simultaneous touch. However, it was also found that the experimental group performed similar to control group on few aspects like temperature, taste, texture and orofacial sensitivity. Apart from the oro-motor abilities which has received utmost interest, the variation in the oro-sensory abilities of experimental and control group is highlighted and discussed in the present study. This emphasizes the need for assessing the oro-sensory abilities in children with developmental dysarthria in addition to oro-motor abilities.

Keywords: cerebral palsy, developmental dysarthria, orosensory assessment, touch

Procedia PDF Downloads 141

Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

Abstract:

The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

Procedia PDF Downloads 275

Authors: Vivian Franco

Abstract:

There has been a great deal of research about vowel production of second language learners of Spanish, vowel variation across Spanish dialects, and more recently, research related to Spanish heritage speakers’ vowel production based on speech style. However, there is little investigation reported on Spanish heritage speakers’ vowel production in regard to task modality by incorporating own comparison groups of monolinguals and late bilinguals. Thus, the present study investigates the influence of speech style on Spanish heritage speakers’ vowel production by comparing Spanish-English early and late bilinguals and Spanish monolinguals. The study was guided by the following research question: How do early bilinguals (heritage speakers) differ/relate to advanced L2 speakers of Spanish (late bilinguals) and Spanish monolinguals in their vowel quality (acoustic distribution) and quantity (duration) based on speech style? The participants were a total of 11 speakers of Spanish: 7 early Spanish-English bilinguals with a similar linguistic background (simultaneous bilinguals of the second generation); 2 advanced L2 speakers of Spanish; and 2 Spanish monolinguals from Mexico. The study consisted of two tasks. The first one adopted a semi-spontaneous style by a solicited narration of life experiences and a description of a favorite movie with the purpose to collect spontaneous speech. The second task was a reading activity in which the participants read two paragraphs of a Mexican literary essay 'La nuez.' This task aimed to obtain a more controlled speech style. From this study, it can be concluded that early bilinguals and monolinguals show a smaller formant vowel space overall compared to the late bilinguals in both speech styles. In terms of formant values by stress, the early bilinguals and the late bilinguals resembled in the semi-spontaneous speech style as their unstressed vowel space overlapped with that of the unstressed vowels different from the monolinguals who displayed a slightly reduced unstressed vowel space. For the controlled data, the early bilinguals were similar to the monolinguals as their stressed and unstressed vowel spaces overlapped in comparison to the late bilinguals who showed a more clear reduction of unstressed vowel space. In regard to stress, the monolinguals revealed longer vowel duration overall. However, findings of duration by stress showed that the early bilinguals and the monolinguals remained stable with shorter values of unstressed vowels in the semi-spontaneous data and longer duration in the controlled data when compared to the late bilinguals who displayed opposite results. These findings suggest an implication for Spanish heritage speakers and L2 Spanish vowels research as it has been frequently argued that Spanish bilinguals differ from the Spanish monolinguals by their vowel reduction and centralized vowel space influenced by English. However, some Spanish varieties are characterized by vowel reduction especially in certain phonetic contexts so that some vowels present more weakening than others. Consequently, it would not be conclusive to affirm an English influence on the Spanish of these bilinguals.

Keywords: Spanish-English bilinguals, Spanish monolinguals, spontaneous and controlled speech, vowel production.

Procedia PDF Downloads 109

Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

Abstract:

Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

Procedia PDF Downloads 120

Authors: Zahra Fotovatnia, Jeffery A. Jones, Alexandra Gottardo

Abstract:

Speech communication often occurs in environments where noise conceals part of a message. Listeners should compensate for the lack of auditory information by picking up distinct acoustic cues and using semantic and sentential context to recreate the speaker’s intended message. This situation seems to be more challenging in a nonnative than native language. On the other hand, early bilinguals are expected to show an advantage over the late bilingual and monolingual speakers of a language due to their better executive functioning components. In this study, English monolingual speakers were compared with early and late nonnative speakers of English to understand speech in noise processing (SIN) and the underlying neurobiological features of this phenomenon. Auditory mismatch negativities (MMNs) were recorded using a double-oddball paradigm in response to a minimal pair that differed in their middle vowel (beat/bit) at Wilfrid Laurier University in Ontario, Canada. The results did not show any significant structural and electroneural differences across groups. However, vocabulary knowledge correlated positively with performance on tests that measured SIN processing in participants who learned English after age 6. Moreover, their performance on the test negatively correlated with the integral area amplitudes in the left superior temporal gyrus (STG). In addition, the STG was engaged before the inferior frontal gyrus (IFG) in noise-free and low-noise test conditions in all groups. We infer that the pre-attentive processing of words engages temporal lobes earlier than the fronto-central areas and that vocabulary knowledge helps the nonnative perception of degraded speech.

Keywords: degraded speech perception, event-related brain potentials, mismatch negativities, brain regions

Procedia PDF Downloads 84

Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

Abstract:

Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

Procedia PDF Downloads 115

Authors: Joy Aworo-Okoroh

Abstract:

Literary texts are mirrors of a real-life situation. Thus, authors choose the linguistic items that would best encode their intended meanings and messages. However, words mean more than they seem. The meaning of words is not static rather, it is dynamic as they constantly enter into relationships within a context. Literary texts can only be meaningful if all pragmatic cues are identified and interpreted. Drawing upon Teun Van Djik's theory of local pragmatic coherence, it is established that words enter into relations in a text and these relations account for sequential speech acts in the texts. Comprehension of the text is dependent on the interpretation of these relations.To show the relevance of pragmatic coherence in literary text analysis, ten conversations were selected in Americanah in order to give a clear idea of the pragmatic relations used. The conversations were analysed, identifying the speech act and epistemic relations inherent in them. A subtle analysis of the structure of the conversations was also carried out. It was discovered that justification is the most commonly used relation and the meaning of the text is dependent on the interpretation of these instances' pragmatic coherence. The study concludes that to effectively teach literature in English, pragmatic coherence should be incorporated as words mean more than they say.

Keywords: pragmatic coherence, epistemic coherence, speech act, Americanah

Procedia PDF Downloads 115

Authors: Ghazal Faraj, Andras Micsik

Abstract:

The CIDOC Conceptual Reference Model (CRM) is an extensible ontology that provides integrated access to heterogeneous and digital datasets. The CIDOC-CRM offers a “semantic glue” intended to promote accessibility to several diverse and dispersed sources of cultural heritage data. That is achieved by providing a formal structure for the implicit and explicit concepts and their relationships in the cultural heritage field. The COURAGE (“Cultural Opposition – Understanding the CultuRal HeritAGE of Dissent in the Former Socialist Countries”) project aimed to explore methods about socialist-era cultural resistance during 1950-1990 and planned to serve as a basis for further narratives and digital humanities (DH) research. This project highlights the diversity of flourished alternative cultural scenes in Eastern Europe before 1989. Moreover, the dataset of COURAGE is an online RDF-based registry that consists of historical people, organizations, collections, and featured items. For increasing the inter-links between different datasets and retrieving more relevant data from various data silos, a shared federated ontology for reconciled data is needed. As a first step towards these goals, a full understanding of the CIDOC CRM ontology (target ontology), as well as the COURAGE dataset, was required to start the work. Subsequently, the queries toward the ontology were determined, and a table of equivalent properties from COURAGE and CIDOC CRM was created. The structural diagrams that clarify the mapping process and construct queries are on progress to map person, organization, and collection entities to the ontology. Through mapping the COURAGE dataset to CIDOC-CRM ontology, the dataset will have a common ontological foundation with several other datasets. Therefore, the expected results are: 1) retrieving more detailed data about existing entities, 2) retrieving new entities’ data, 3) aligning COURAGE dataset to a standard vocabulary, 4) running distributed SPARQL queries over several CIDOC-CRM datasets and testing the potentials of distributed query answering using SPARQL. The next plan is to map CIDOC-CRM to other upper-level ontologies or large datasets (e.g., DBpedia, Wikidata), and address similar questions on a wide variety of knowledge bases.

Keywords: CIDOC CRM, cultural heritage data, COURAGE dataset, ontology alignment

Procedia PDF Downloads 125

Authors: Tiago do Carmo Nogueira, Cássio Dener Noronha Vinhal, Gélson da Cruz Júnior, Matheus Rudolfo Diedrich Ullmann

Abstract:

Generating automatic image descriptions through natural language is a challenging task. Image captioning is a task that consistently describes an image by combining computer vision and natural language processing techniques. To accomplish this task, cutting-edge models use encoder-decoder structures. Thus, Convolutional Neural Networks (CNN) are used to extract the characteristics of the images, and Recurrent Neural Networks (RNN) generate the descriptive sentences of the images. However, cutting-edge approaches still suffer from problems of generating incorrect captions and accumulating errors in the decoders. To solve this problem, we propose a model based on the encoder-decoder structure, introducing a module that generates the weights according to the importance of the word to form the sentence, using the part-of-speech (PoS). Thus, the results demonstrate that our model surpasses state-of-the-art models.

Keywords: gated recurrent units, caption generation, convolutional neural network, part-of-speech

Procedia PDF Downloads 76

Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

Procedia PDF Downloads 298

Authors: Alimohamad Asghari, Ahmad Daneshi, Mohammad Farhadi, Arash Bayat, Mohammad Ajalloueyan, Marjan Mirsalehi, Mohsen Rajati, Seyed Basir Hashemi, Nader Saki, Ali Omidvari

Abstract:

Evidence suggests that Cochlear Implantation (CI) is a beneficial approach for auditory and speech skills improvement in children with severe to profound hearing loss. However, it remains controversial if implantation in children <12 months is safe and effective compared to older children. The present study aimed to determine whether children's ages affect surgical complications and auditory and speech development. The current multicenter study enrolled 86 children who underwent CI surgery at <12 months of age (group A) and 362 children who underwent implantation between 12 and 24 months of age (group B). The Categories of Auditory Performance (CAP) and Speech Intelligibility Rating (SIR) scores were determined pre-impanation, and "one-year" and "two-year" post-implantation. Four complications (overall rate: 4.65%; three minor) occurred in group A and 12 complications (overall rate: 4.41%; nine minor) occurred in group B. We found no statistically significant difference in the complication rates between the groups (p>0.05). The mean SIR and CAP scores improved over time following CI activation in both groups. However, we did not find significant differences in CAP and SIR scores between the groups across different time points. Cochlear implantation is a safe and efficient procedure in children younger than 12 months, providing substantial auditory and speech benefits comparable to children undergoing implantation at 12 to 24 months of age. Furthermore, surgical complications in younger children are similar to those of children undergoing the CI at an older age.

Keywords: cochlear implant, Infant, complications, outcome

Procedia PDF Downloads 85

Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

Abstract:

This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

Procedia PDF Downloads 15

Authors: Sadeq Al Yaari, Fayza Al Hammadi, Ayman Al Yaari, Adham Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Sajedah Al Yaari, Salah Al Yami

Abstract:

Background: When we further take Arabic grammatical issues into account in accordance with applied linguistic investigations on Arabic as Second Language (ASL) learners, a fundamental issue arises at this point as to the production of speech in Arabic: Oral grammatical errors committed by ASL learners. Aims: Using manual rating as well as computational analytic methodology to test a corpus of recorded speech by Second Language (ASL) learners of Arabic, this study aims to find the areas of difficulties in learning Arabic grammar. More specifically, it examines how and why ASL learners make grammatical errors in their oral speech. Methods: Tape recordings of four (4) Arabic as Second Language (ASL) learners who ranged in age from 23 to 30 were naturally collected. All participants have completed an intensive Arabic program (two years) and 20 minute-speech was recorded for each participant. Having the collected corpus, the next procedure was to rate them against Arabic standard grammar. The rating includes four processes: Description, analysis and assessment. Conclusions: Outcomes made from the issues addressed in this paper can be summarized in the fact that ASL learners face many grammatical difficulties when studying Arabic word order, tenses and aspects, function words, subject-verb agreement, verb form, active-passive voice, global and local errors, processes-based errors including addition, omission, substitution or a combination of any of them.

Keywords: grammar, error, oral, Arabic, second language, learner, applied linguistics.

Procedia PDF Downloads 12

Authors: Albatool Al Abdulwahid, Bayan Shakally, Mariam Mohamed, Wed Almokri

Abstract:

Artificial intelligence has infiltrated every part of our life and every field we can think of. With technical developments, artificial intelligence applications are becoming more prevalent. We chose ELSA speak because it is a magnificent example of Artificial intelligent applications, ELSA speak is a smartphone application that is free to download on both IOS and Android smartphones. ELSA speak utilizes artificial intelligence to help non-native English speakers pronounce words and phrases similar to a native speaker, as well as enhance their English skills. It employs speech-recognition technology that aids the application to excel the pronunciation of its users. This remarkable feature distinguishes ELSA from other voice recognition algorithms and increase the efficiency of the application. This study focused on evaluating ELSA speak application, by testing the degree of effectiveness based on survey questions. The results of the questionnaire were variable. The generality of the participants strongly agreed that ELSA has helped them enhance their pronunciation skills. However, a few participants were unconfident about the application’s ability to assist them in their learning journey.

Keywords: ELSA speak application, artificial intelligence, speech-recognition technology, language learning, english pronunciation

Procedia PDF Downloads 81

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

Abstract:

Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

Procedia PDF Downloads 70

Authors: Vishal Raj, Noorhan Abbas

Abstract:

Ambiguity in NLP (Natural language processing) refers to the ability of a word, phrase, sentence, or text to have multiple meanings. This results in various kinds of ambiguities such as lexical, syntactic, semantic, anaphoric and referential am-biguities. This study is focused mainly on solving the issue of Lexical ambiguity. Word Sense Disambiguation (WSD) is an NLP technique that aims to resolve lexical ambiguity by determining the correct meaning of a word within a given context. Most WSD solutions rely on words for training and testing, but we have used lemma and Part of Speech (POS) tokens of words for training and testing. Lemma adds generality and POS adds properties of word into token. We have designed a novel method to create an affinity matrix to calculate the affinity be-tween any pair of lemma_POS (a token where lemma and POS of word are joined by underscore) of given training set. Additionally, we have devised an al-gorithm to create the sense clusters of tokens using affinity matrix under hierar-chy of POS of lemma. Furthermore, three different mechanisms to predict the sense of target word using the affinity/similarity value are devised. Each contex-tual token contributes to the sense of target word with some value and whichever sense gets higher value becomes the sense of target word. So, contextual tokens play a key role in creating sense clusters and predicting the sense of target word, hence, the model is named Contextual SenSe Model (CSM). CSM exhibits a noteworthy simplicity and explication lucidity in contrast to contemporary deep learning models characterized by intricacy, time-intensive processes, and chal-lenging explication. CSM is trained on SemCor training data and evaluated on SemEval test dataset. The results indicate that despite the naivety of the method, it achieves promising results when compared to the Most Frequent Sense (MFS) model.

Keywords: word sense disambiguation (wsd), contextual sense model (csm), most frequent sense (mfs), part of speech (pos), natural language processing (nlp), oov (out of vocabulary), lemma_pos (a token where lemma and pos of word are joined by underscore), information retrieval (ir), machine translation (mt)

Procedia PDF Downloads 82

Authors: Arpit Rai

Abstract:

In this work, we propose PatchMix, a semi-supervised method for pre-training visual representations. PatchMix mixes patches of two images and then solves an auxiliary task of predicting the label of each patch in the mixed image. Our experiments on the CIFAR-10, 100 and the SVHN dataset show that the representations learned by this method encodes useful information for transfer to new tasks and outperform the baseline Residual Network encoders by on CIFAR 10 by 12% on ResNet 101 and 2% on ResNet-56, by 4% on CIFAR-100 on ResNet101 and by 6% on SVHN dataset on the ResNet-101 baseline model.

Keywords: self-supervised learning, representation learning, computer vision, generalization

Procedia PDF Downloads 66

Authors: E. Tchandao Mangamana, V. Cariou, E. Vigneau, R. Glele Kakai, E. M. Qannari

Abstract:

A new definition of a latent variable associated with a dataset makes it possible to propose variants of the PLS2 regression and the multi-block PLS (MB-PLS). We shall refer to these variants as Rd-PLS regression and Rd-MB-PLS respectively because they are inspired by both Redundancy analysis and PLS regression. Usually, a latent variable t associated with a dataset Z is defined as a linear combination of the variables of Z with the constraint that the length of the loading weights vector equals 1. Formally, t=Zw with ‖w‖=1. Denoting by Z' the transpose of Z, we define herein, a latent variable by t=ZZ’q with the constraint that the auxiliary variable q has a norm equal to 1. This new definition of a latent variable entails that, as previously, t is a linear combination of the variables in Z and, in addition, the loading vector w=Z’q is constrained to be a linear combination of the rows of Z. More importantly, t could be interpreted as a kind of projection of the auxiliary variable q onto the space generated by the variables in Z, since it is collinear to the first PLS1 component of q onto Z. Consider the situation in which we aim to predict a dataset Y from another dataset X. These two datasets relate to the same individuals and are assumed to be centered. Let us consider a latent variable u=YY’q to which we associate the variable t= XX’YY’q. Rd-PLS consists in seeking q (and therefore u and t) so that the covariance between t and u is maximum. The solution to this problem is straightforward and consists in setting q to the eigenvector of YY’XX’YY’ associated with the largest eigenvalue. For the determination of higher order components, we deflate X and Y with respect to the latent variable t. Extending Rd-PLS to the context of multi-block data is relatively easy. Starting from a latent variable u=YY’q, we consider its ‘projection’ on the space generated by the variables of each block Xk (k=1, ..., K) namely, tk= XkXk'YY’q. Thereafter, Rd-MB-PLS seeks q in order to maximize the average of the covariances of u with tk (k=1, ..., K). The solution to this problem is given by q, eigenvector of YY’XX’YY’, where X is the dataset obtained by horizontally merging datasets Xk (k=1, ..., K). For the determination of latent variables of order higher than 1, we use a deflation of Y and Xk with respect to the variable t= XX’YY’q. In the same vein, extending Rd-MB-PLS to the path modeling setting is straightforward. Methods are illustrated on the basis of case studies and performance of Rd-PLS and Rd-MB-PLS in terms of prediction is compared to that of PLS2 and MB-PLS.

Keywords: multiblock data analysis, partial least squares regression, path modeling, redundancy analysis

Procedia PDF Downloads 117

Authors: T. Thein, S. Kalyar Myo

Abstract:

Human uses visual information for understanding the speech contents in noisy conditions or in situations where the audio signal is not available. The primary advantage of visual information is that it is not affected by the acoustic noise and cross talk among speakers. Using visual information from the lip movements can improve the accuracy and robustness of automatic speech recognition. However, a major challenge with most automatic lip reading system is to find a robust and efficient method for extracting the linguistically relevant speech information from a lip image sequence. This is a difficult task due to variation caused by different speakers, illumination, camera setting and the inherent low luminance and chrominance contrast between lip and non-lip region. Several researchers have been developing methods to overcome these problems; the one is lip reading. Moreover, it is well known that visual information about speech through lip reading is very useful for human speech recognition system. Lip reading is the technique of a comprehensive understanding of underlying speech by processing on the movement of lips. Therefore, lip reading system is one of the different supportive technologies for hearing impaired or elderly people, and it is an active research area. The need for lip reading system is ever increasing for every language. This research aims to develop a visual teaching method system for the hearing impaired persons in Myanmar, how to pronounce words precisely by identifying the features of lip movement. The proposed research will work a lip reading system for Myanmar Consonants, one syllable consonants (င (Nga)၊ ည (Nya)၊ မ (Ma)၊ လ (La)၊ ၀ (Wa)၊ သ (Tha)၊ ဟ (Ha)၊ အ (Ah) ) and two syllable consonants ( က(Ka Gyi)၊ ခ (Kha Gway)၊ ဂ (Ga Nge)၊ ဃ (Ga Gyi)၊ စ (Sa Lone)၊ ဆ (Sa Lain)၊ ဇ (Za Gwe) ၊ ဒ (Da Dway)၊ ဏ (Na Gyi)၊ န (Na Nge)၊ ပ (Pa Saug)၊ ဘ (Ba Gone)၊ ရ (Ya Gaug)၊ ဠ (La Gyi) ). In the proposed system, there are three subsystems, the first one is the lip localization system, which localizes the lips in the digital inputs. The next one is the feature extraction system, which extracts features of lip movement suitable for visual speech recognition. And the final one is the classification system. In the proposed research, Two Dimensional Discrete Cosine Transform (2D-DCT) and Linear Discriminant Analysis (LDA) with Active Contour Model (ACM) will be used for lip movement features extraction. Support Vector Machine (SVM) classifier is used for finding class parameter and class number in training set and testing set. Then, experiments will be carried out for the recognition accuracy of Myanmar consonants using the only visual information on lip movements which are useful for visual speech of Myanmar languages. The result will show the effectiveness of the lip movement recognition for Myanmar Consonants. This system will help the hearing impaired persons to use as the language learning application. This system can also be useful for normal hearing persons in noisy environments or conditions where they can find out what was said by other people without hearing voice.

Keywords: feature extraction, lip reading, lip localization, Active Contour Model (ACM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Two Dimensional Discrete Cosine Transform (2D-DCT)

Procedia PDF Downloads 266

Authors: Shraddha Chaudhary, Raksha Agarwal, Niladri Chatterjee

Abstract:

This work demonstrates a web crawler-based generalized end-to-end open domain Question Answering (QA) system. An efficient QA system requires a significant amount of domain knowledge to answer any question with the aim to find an exact and correct answer in the form of a number, a noun, a short phrase, or a brief piece of text for the user's questions. Analysis of the question, searching the relevant document, and choosing an answer are three important steps in a QA system. This work uses a web scraper (Beautiful Soup) to extract K-documents from the web. The value of K can be calibrated on the basis of a trade-off between time and accuracy. This is followed by a passage ranking process using the MS-Marco dataset trained on 500K queries to extract the most relevant text passage, to shorten the lengthy documents. Further, a QA system is used to extract the answers from the shortened documents based on the query and return the top 3 answers. For evaluation of such systems, accuracy is judged by the exact match between predicted answers and gold answers. But automatic evaluation methods fail due to the linguistic ambiguities inherent in the questions. Moreover, reference answers are often not exhaustive or are out of date. Hence correct answers predicted by the system are often judged incorrect according to the automated metrics. One such scenario arises from the original Google Natural Question (GNQ) dataset which was collected and made available in the year 2016. Use of any such dataset proves to be inefficient with respect to any questions that have time-varying answers. For illustration, if the query is where will be the next Olympics? Gold Answer for the above query as given in the GNQ dataset is “Tokyo”. Since the dataset was collected in the year 2016, and the next Olympics after 2016 were in 2020 that was in Tokyo which is absolutely correct. But if the same question is asked in 2022 then the answer is “Paris, 2024”. Consequently, any evaluation based on the GNQ dataset will be incorrect. Such erroneous predictions are usually given to human evaluators for further validation which is quite expensive and time-consuming. To address this erroneous evaluation, the present work proposes an automated approach for evaluating time-dependent question-answer pairs. In particular, it proposes a metric using the current timestamp along with top-n predicted answers from a given QA system. To test the proposed approach GNQ dataset has been used and the system achieved an accuracy of 78% for a test dataset comprising 100 QA pairs. This test data was automatically extracted using an analysis-based approach from 10K QA pairs of the GNQ dataset. The results obtained are encouraging. The proposed technique appears to have the possibility of developing into a useful scheme for gathering precise, reliable, and specific information in a real-time and efficient manner. Our subsequent experiments will be guided towards establishing the efficacy of the above system for a larger set of time-dependent QA pairs.

Keywords: web-based information retrieval, open domain question answering system, time-varying QA, QA evaluation

Procedia PDF Downloads 82

Authors: Rasha Salameh

Abstract:

In light of the tense relationship between Saudi Arabia and Iran, this research paper sheds some light on Al-Arabiya’s reporting of Coronavirus in the Gulf. Particularly because most of the cases, in the beginning, were coming from Iran, some programs of this Saudi channel embraced a conspiracy theory. Hate speech has been used in talking about the topic and discussing it. The results of these discussions will be detailed in this paper in percentages with regard to the research sample, which includes five programs on Al-Arabiya channel: ‘DNA’, ‘Marraya’ (Mirrors), ‘Panorama’, ‘Tafaolcom’ (Your Interaction) and the ‘Diplomatic Street’, in the period between January 19, that is, the date of the first case in Iran, and April 10, 2020. The research shows the use of a conspiracy theory in the programs, in addition to some professional violations. The surveyed sample also shows that the matter receded due to the Arab Gulf states' preoccupation with the successively increasing cases that have appeared there since the start of the pandemic. The results indicate that hate speech was present in the sample at a rate of 98.1% and that most of the programs that dealt with the Iranian issue under the Corona pandemic on Al Arabiya used the conspiracy theory at a rate of 75.5%.

Keywords: Al-Arabiya, Iran, Corona, hate speech, conspiracy theory, politicization of the pandemic

Procedia PDF Downloads 117

Authors: Shantanu Arya, Sachin Sakhuja, Gunjan Mehta, Sanjay Munjal

Abstract:

Stuttering may be defined as a speech disorder affecting the fluency domain of speech and characterized by covert features like word substitution, omittance and circumlocution and overt features like prolongation of sound, syllables and blocks etc. Many etiologies have been postulated to explain stuttering based on various experiments and research. Moreover, Breathlessness has also been reported by many individuals with stuttering for which breathing exercises are generally advised. However, no studies reporting objective evaluation of the pulmonary capacity and further objective assessment of the efficacy of breathing exercises have been conducted. Pulmonary Function Test which evaluates parameters like Forced Vital Capacity, Peak Expiratory Flow Rate, Forced expiratory flow Rate can be used to study the pulmonary behavior of individuals with stuttering. The study aimed: a) To identify speech motor & physiologic behaviours associated with stuttering by administering PFT. b) To recognize possible reasons for an association between speech motor behaviour & stuttering severity. In this regard, PFT tests were administered on individuals who reported signs and symptoms of stuttering and showed abnormal scores on Stuttering Severity Index. Parameters like Forced Vital Capacity, Forced Expiratory Volume, Peak Expiratory Flow Rate (L/min), Forced Expiratory Flow Rate (L/min) were evaluated and correlated with scores of Stuttering Severity Index. Results showed significant decrease in the parameters (lower than normal scores) in individuals with established stuttering. Strong correlation was also found between degree of stuttering and the degree of decrease in the pulmonary volumes. Thus, it is evident that fluent speech requires strong support of lung pressure and requisite volumes. Further research in demonstrating the efficacy of abdominal breathing exercises in this regard is needed.

Keywords: forced expiratory flow rate, forced expiratory volume, forced vital capacity, peak expiratory flow rate, stuttering

Procedia PDF Downloads 247

Authors: Shakila N. Senarath, Dinesh Asanka, Janaka Wijayanayake

Abstract:

The necessity of cosmetic products is arising to fulfill consumer needs of personality appearance and hygiene. A cosmetic product consists of various chemical ingredients which may help to keep the skin healthy or may lead to damages. Every chemical ingredient in a cosmetic product does not perform on every human. The most appropriate way to select a healthy cosmetic product is to identify the texture of the body first and select the most suitable product with safe ingredients. Therefore, the selection process of cosmetic products is complicated. Consumer surveys have shown most of the time, the selection process of cosmetic products is done in an improper way by consumers. From this study, a content-based system is suggested that recommends cosmetic products for the human factors. To such an extent, the skin type, gender and price range will be considered as human factors. The proposed system will be implemented by using Machine Learning. Consumer skin type, gender and price range will be taken as inputs to the system. The skin type of consumer will be derived by using the Baumann Skin Type Questionnaire, which is a value-based approach that includes several numbers of questions to derive the user’s skin type to one of the 16 skin types according to the Bauman Skin Type indicator (BSTI). Two datasets are collected for further research proceedings. The user data set was collected using a questionnaire given to the public. Those are the user dataset and the cosmetic dataset. Product details are included in the cosmetic dataset, which belongs to 5 different kinds of product categories (Moisturizer, Cleanser, Sun protector, Face Mask, Eye Cream). An alternate approach of TF-IDF (Term Frequency – Inverse Document Frequency) is applied to vectorize cosmetic ingredients in the generic cosmetic products dataset and user-preferred dataset. Using the IF-IPF vectors, each user-preferred products dataset and generic cosmetic products dataset can be represented as sparse vectors. The similarity between each user-preferred product and generic cosmetic product will be calculated using the cosine similarity method. For the recommendation process, a similarity matrix can be used. Higher the similarity, higher the match for consumer. Sorting a user column from similarity matrix in a descending order, the recommended products can be retrieved in ascending order. Even though results return a list of similar products, and since the user information has been gathered, such as gender and the price ranges for product purchasing, further optimization can be done by considering and giving weights for those parameters once after a set of recommended products for a user has been retrieved.

Keywords: content-based filtering, cosmetics, machine learning, recommendation system

Procedia PDF Downloads 114

Authors: Seham El Kareh, Miramar Etman

Abstract:

Recently, detecting liars and extracting features which distinguish them from truth-tellers have been the focus of a wide range of disciplines. To the author’s best knowledge, most of the work has been done on facial expressions and body gestures but only few works have been done on the language used by both liars and truth-tellers. This paper sheds light on four axes. The first axis copes with building an audio corpus for deceptive and truthful speech for Egyptian Arabic speakers. The second axis focuses on examining the human perception of lies and proving our need for computational linguistic-based methods to extract features which characterize truthful and deceptive speech. The third axis is concerned with building a linguistic analysis program that could extract from the corpus the inter- and intra-linguistic cues for deceptive and truthful speech. The program built here is based on selected categories from the Linguistic Inquiry and Word Count program. Our results demonstrated that Egyptian Arabic speakers on one hand preferred to use first-person pronouns and present tense compared to the past tense when lying and their lies lacked of second-person pronouns, and on the other hand, when telling the truth, they preferred to use the verbs related to motion and the nouns related to time. The results also showed that there is a need for bigger data to prove the significance of words related to emotions and numbers.

Keywords: Egyptian Arabic corpus, computational analysis, deceptive features, forensic linguistics, human perception, truthful features

Procedia PDF Downloads 187