Search results for: Speech Coder.

Authors: Ing-Jr Ding

Abstract:

This paper presents a technical speaker adaptation method called WMLLR, which is based on maximum likelihood linear regression (MLLR). In MLLR, a linear regression-based transform which adapted the HMM mean vectors was calculated to maximize the likelihood of adaptation data. In this paper, the prior knowledge of the initial model is adequately incorporated into the adaptation. A series of speaker adaptation experiments are carried out at a 30 famous city names database to investigate the efficiency of the proposed method. Experimental results show that the WMLLR method outperforms the conventional MLLR method, especially when only few utterances from a new speaker are available for adaptation.

Keywords: hidden Markov model, maximum likelihood linearregression, speech recognition, speaker adaptation.

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Authors: Kashif Bilal, Uzair Muhammad, Atif Khan, M. Nasir Khan

Abstract:

Machine Translation, (hereafter in this document referred to as the "MT") faces a lot of complex problems from its origination. Extracting multiword expressions is also one of the complex problems in MT. Finding multiword expressions during translating a sentence from English into Urdu, through existing solutions, takes a lot of time and occupies system resources. We have designed a simple relational data approach, in which we simply set a bit in dictionary (database) for multiword, to find and handle multiword expression. This approach handles multiword efficiently.

Keywords: Machine Translation, Multiword Expressions, Urdulanguage processing, POS (stands for Parts of Speech) Tagging forUrdu, Expert Systems.

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Authors: Junseok Ham, Chansun Jung, Junhyung Park, Jihye Ryeo, Ilju Ko

Abstract:

It is hard to express emotion through only speech when we watch a character in a movie or a play because we cannot estimate the size, kind, and quantity of emotion. So this paper proposes an artificial emotion model for visualizing current emotion with color and location in emotion model. The artificial emotion model is designed considering causality of generated emotion, difference of personality, difference of continual emotional stimulus, and co-relation of various emotions. This paper supposed the Emotion Field for visualizing current emotion with location, and current emotion is expressed by location and color in the Emotion Field. For visualizing changes within current emotion, the artificial emotion model is adjusted to characters in Hamlet.

Keywords: Emotion, Artificial Emotion, Visualizing, EmotionModel.

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Authors: Negar Riazifar, Nigel G. Stocks

Abstract:

This paper proposes strategies in level crossing (LC) sampling and reconstruction that provide alias-free high-fidelity signal reconstruction for speech signals without exponentially increasing sample number with increasing bit-depth. We introduce methods in LC sampling that reduce the sampling rate close to the Nyquist frequency even for large bit-depth. The results indicate that larger variation in the sampling intervals leads to alias-free sampling scheme; this is achieved by either reducing the bit-depth or adding a jitter to the system for high bit-depths. In conjunction with windowing, the signal is reconstructed from the LC samples using an efficient Toeplitz reconstruction algorithm.

Keywords: Alias-free, level crossing sampling, spectrum, trigonometric polynomial.

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Authors: Mehran Yazdi, Mehdi Seyfi, Amirhossein Rafati, Meghdad Asadi

Abstract:

Detection and tracking of the lip contour is an important issue in speechreading. While there are solutions for lip tracking once a good contour initialization in the first frame is available, the problem of finding such a good initialization is not yet solved automatically, but done manually. We have developed a new tracking solution for lip contour detection using only few landmarks (15 to 25) and applying the well known Active Shape Models (ASM). The proposed method is a new LMS-like adaptive scheme based on an Auto regressive (AR) model that has been fit on the landmark variations in successive video frames. Moreover, we propose an extra motion compensation model to address more general cases in lip tracking. Computer simulations demonstrate a fair match between the true and the estimated spatial pixels. Significant improvements related to the well known LMS approach has been obtained via a defined Frobenius norm index.

Keywords: Lip contour, Tracking, LMS-Like

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Authors: Said Boularouk, Didier Josselin, Eitan Altman

Abstract:

In this paper, we present a vocal ontology of OpenStreetMap data for the apprehension of space by visually impaired people. Indeed, the platform based on produsage gives a freedom to data producers to choose the descriptors of geocoded locations. Unfortunately, this freedom, called also folksonomy leads to complicate subsequent searches of data. We try to solve this issue in a simple but usable method to extract data from OSM databases in order to send them to visually impaired people using Text To Speech technology. We focus on how to help people suffering from visual disability to plan their itinerary, to comprehend a map by querying computer and getting information about surrounding environment in a mono-modal human-computer dialogue.

Keywords: Ontology, OpenStreetMap, visually impaired people, TTS, taxonomy.

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Authors: Masaru Fujieda, Takahiro Murakami, Yoshihisa Ishida

Abstract:

Independent component analysis (ICA) in the frequency domain is used for solving the problem of blind source separation (BSS). However, this method has some problems. For example, a general ICA algorithm cannot determine the permutation of signals which is important in the frequency domain ICA. In this paper, we propose an approach to the solution for a permutation problem. The idea is to effectively combine two conventional approaches. This approach improves the signal separation performance by exploiting features of the conventional approaches. We show the simulation results using artificial data.

Keywords: Blind source separation, Independent componentanalysis, Frequency domain, Permutation ambiguity.

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Authors: E. S. Gower, M. O. J. Hawksford

Abstract:

An algorithm for learning an overcomplete dictionary using a Cauchy mixture model for sparse decomposition of an underdetermined mixing system is introduced. The mixture density function is derived from a ratio sample of the observed mixture signals where 1) there are at least two but not necessarily more mixture signals observed, 2) the source signals are statistically independent and 3) the sources are sparse. The basis vectors of the dictionary are learned via the optimization of the location parameters of the Cauchy mixture components, which is shown to be more accurate and robust than the conventional data mining methods usually employed for this task. Using a well known sparse decomposition algorithm, we extract three speech signals from two mixtures based on the estimated dictionary. Further tests with additive Gaussian noise are used to demonstrate the proposed algorithm-s robustness to outliers.

Keywords: expectation-maximization, Pitman estimator, sparsedecomposition

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Authors: Jaehwan Kang, Haejung Lee

Abstract:

This paper introduces an automatic voice classification system for the diagnosis of individual constitution based on Sasang Constitutional Medicine (SCM) in Traditional Korean Medicine (TKM). For the developing of this algorithm, we used the voices of 309 female speakers and extracted a total of 134 speech features from the voice data consisting of 5 sustained vowels and one sentence. The classification system, based on a rule-based algorithm that is derived from a non parametric statistical method, presents 3 types of decisions: reserved, positive and negative decisions. In conclusion, 71.5% of the voice data were diagnosed by this system, of which 47.7% were correct positive decisions and 69.7% were correct negative decisions.

Keywords: Voice Classifier, Sasang Constitution Medicine, Traditional Korean Medicine, SCM, TKM.

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Authors: Yasuhiko Okuma, Yuichi Suzuki, Takahiro Murakami, Yoshihisa Ishida

Abstract:

In this paper, an extended method of the directionally constrained minimization of power (DCMP) algorithm for broadband signals is proposed. The DCMP algorithm is one of the useful techniques of extracting a target signal from observed signals of a microphone array system. In the DCMP algorithm, output power of the microphone array is minimized under a constraint of constant responses to directions of arrival (DOAs) of specific signals. In our algorithm, by limiting the directional constraint to the perpendicular direction to the sensor array system, the calculating time is reduced.

Keywords: Beamformer, directionally constrained minimizationof power, direction of arrival, microphone array.

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Authors: So-Jeong Youn, Kyung-Whan Oh

Abstract:

The human friendly interaction is the key function of a human-centered system. Over the years, it has received much attention to develop the convenient interaction through intention recognition. Intention recognition processes multimodal inputs including speech, face images, and body gestures. In this paper, we suggest a novel approach of intention recognition using a graph representation called Intention Graph. A concept of valid intention is proposed, as a target of intention recognition. Our approach has two phases: goal recognition phase and intention recognition phase. In the goal recognition phase, we generate an action graph based on the observed actions, and then the candidate goals and their plans are recognized. In the intention recognition phase, the intention is recognized with relevant goals and user profile. We show that the algorithm has polynomial time complexity. The intention graph is applied to a simple briefcase domain to test our model.

Keywords: Intention recognition, intention, graph, HCI.

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Authors: Robert Krause

Abstract:

Primary progressive aphasia (PPA) is a neurodegenerative disease similar to frontotemporal and semantic dementia, while having a different clinical image and anatomic pathology topography. Nonetheless, they are often included under an umbrella term: frontotemporal lobar degeneration (FTLD). In the study, examples of diagnosing PPA are presented through the multidisciplinary lens of specialists from different fields (neurologists, psychiatrists, clinical speech therapists, clinical neuropsychologists and others) using a variety of diagnostic tools such as MR, PET/CT, genetic screening and neuropsychological and logopedic methods. Thanks to that, specialists can get a better and clearer understanding of PPA diagnosis. The study summarizes the concrete procedures and results of different specialists while diagnosing PPA in a patient of younger middle age and illustrates the importance of multidisciplinary approach to differential diagnosis of PPA.

Keywords: Primary progressive aphasia, etiology, diagnosis, younger middle age.

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Authors: Sang-Wan Kim, Hoon Lee, Kyung-Ho Choi, Soon-Young Park

Abstract:

A talking head system (THS) is presented to animate the face of a speaking 3D avatar in such a way that it realistically pronounces the given Korean text. The proposed system consists of SAPI compliant text-to-speech (TTS) engine and MPEG-4 compliant face animation generator. The input to the THS is a unicode text that is to be spoken with synchronized lip shape. The TTS engine generates a phoneme sequence with their duration and audio data. The TTS applies the coarticulation rules to the phoneme sequence and sends a mouth animation sequence to the face modeler. The proposed THS can make more natural lip sync and facial expression by using the face animation generator than those using the conventional visemes only. The experimental results show that our system has great potential for the implementation of talking head for Korean text.

Keywords: Talking head, Lip sync, TTS, MPEG4.

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Authors: Bruna Luisa Ramos Prado Vasques, Mariane Rembold Petraglia, Antonio Petraglia

Abstract:

This paper investigates MIMO (Multiple-Input Multiple-Output) adaptive filtering techniques for the application of supervised source separation in the context of convolutive mixtures. From the observation that there is correlation among the signals of the different mixtures, an improvement in the NSAF (Normalized Subband Adaptive Filter) algorithm is proposed in order to accelerate its convergence rate. Simulation results with mixtures of speech signals in reverberant environments show the superior performance of the proposed algorithm with respect to the performances of the NLMS (Normalized Least-Mean-Square) and conventional NSAF, considering both the convergence speed and SIR (Signal-to-Interference Ratio) after convergence.

Keywords: Adaptive filtering, multirate processing, normalized subband adaptive filter, source separation.

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Authors: Rafik Djemili, Mouldi Bedda, Hocine Bourouba

Abstract:

This paper proposes a novel approach that combines statistical models and support vector machines. A hybrid scheme which appropriately incorporates the advantages of both the generative and discriminant model paradigms is described and evaluated. Support vector machines (SVMs) are trained to divide the whole speakers' space into small subsets of speakers within a hierarchical tree structure. During testing a speech token is assigned to its corresponding group and evaluation using gaussian mixture models (GMMs) is then processed. Experimental results show that the proposed method can significantly improve the performance of text independent speaker identification task. We report improvements of up to 50% reduction in identification error rate compared to the baseline statistical model.

Keywords: Speaker identification, Gaussian mixture model (GMM), support vector machine (SVM), hybrid GMM/SVM.

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Authors: Rustam Asnawi, Wan Fatimah Wan Ahmad

Abstract:

Currently electronic slide (e-slide) is one of the most common styles in educational presentation. Unfortunately, the utilization of e-slide for the visually impaired is uncommon since they are unable to see the content of such e-slides which are usually composed of text, images and animation. This paper proposes a model for presenting e-slide in multimodal presentation i.e. using conventional slide concurrent with voicing, in both languages Malay and English. At the design level, live multimedia presentation concept is used, while at the implementation level several components are used. The text content of each slide is extracted using COM component, Microsoft Speech API for voicing the text in English language and the text in Malay language is voiced using dictionary approach. To support the accessibility, an auditory user interface is provided as an additional feature. A prototype of such model named as VSlide has been developed and introduced.

Keywords: presentation, self-learning, slide, visually impaired

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Authors: Somsri Binraman, Boonliang Kaewnapan, Krittika Tanprasert

Abstract:

In the package design industry, there are a lot of tacit knowledge resided within each designer. The objectives are to capture them and compile it to be used as a teaching resource and to create a video clip of package design process as well as to evaluate its quality and learning effectiveness. Interview were used as a technique for capturing knowledge in brand design concept, differentiation, recognition, rank of recognition factor, consumer survey, knowledge about marketing, research, graphic design, the effect of color, and law and regulation. Video clip about package design were created. The clip consisted of both the speech and clip of actual process. The quality of the video in term of media was ranked as good while the content was ranked as excellent. The students- score on post-test was significantly greater than that of pretest (p>0.001).

Keywords: Tacit knowledge, interview, video, packaging, design.

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Authors: Tin Hninn Hninn Maung

Abstract:

This paper introduces a hand gesture recognition system to recognize real time gesture in unstrained environments. Efforts should be made to adapt computers to our natural means of communication: Speech and body language. A simple and fast algorithm using orientation histograms will be developed. It will recognize a subset of MAL static hand gestures. A pattern recognition system will be using a transforrn that converts an image into a feature vector, which will be compared with the feature vectors of a training set of gestures. The final system will be Perceptron implementation in MATLAB. This paper includes experiments of 33 hand postures and discusses the results. Experiments shows that the system can achieve a 90% recognition average rate and is suitable for real time applications.

Keywords: Hand gesture recognition, Orientation Histogram, Myanmar Alphabet Language, Perceptronnetwork, MATLAB.

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Authors: Nermine Ahmed Hendy, Hania Farag

Abstract:

Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time

Keywords: Classification, emotion recognition, features extraction, feature selection, neural network

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Authors: Abhijit Mitra

Abstract:

Different pseudo-random or pseudo-noise (PN) as well as orthogonal sequences that can be used as spreading codes for code division multiple access (CDMA) cellular networks or can be used for encrypting speech signals to reduce the residual intelligence are investigated. We briefly review the theoretical background for direct sequence CDMA systems and describe the main characteristics of the maximal length, Gold, Barker, and Kasami sequences. We also discuss about variable- and fixed-length orthogonal codes like Walsh- Hadamard codes. The equivalence of PN and orthogonal codes are also derived. Finally, a new PN sequence is proposed which is shown to have certain better properties than the existing codes.

Keywords: Code division multiple access, pseudo-noise codes, maximal length, Gold, Barker, Kasami, Walsh-Hadamard, autocorrelation, crosscorrelation, figure of merit.

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Authors: Sergei Aleinik, Mikhail Stolbov

Abstract:

In this work, a method of time delay estimation for  dual-channel acoustic signals (speech, music, etc.) recorded under  reverberant conditions is investigated. Standard methods based on  cross-correlation of the signals show poor results in cases involving  strong reverberation, large distances between microphones and  asynchronous recordings. Under similar conditions, a method based  on cross-correlation of temporal envelopes of the signals delivers a  delay estimation of acceptable quality. This method and its properties  are described and investigated in detail, including its limits of  applicability. The method’s optimal parameter estimation and a  comparison with other known methods of time delay estimation are  also provided.

 

Keywords: Cross-correlation, delay estimation, signal envelope, signal processing.

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Authors: Yu Luan

Abstract:

The bone-conducted objective occlusion effect (OE) is characterized by a discomforting sensation of fullness experienced in an occluded ear. This phenomenon arises from various external stimuli, such as human speech, chewing, and walking, which generate vibrations transmitted through the body to the ear canal walls. The bone-conducted OE occurs due to the pressure build-up inside the occluded ear caused by sound radiating into the ear canal cavity from its walls. In the hearing aid industry, artificial ears are utilized as a tool for developing hearing aids. However, the currently available commercial artificial ears primarily focus on pure acoustics measurements, neglecting the bone-conducted vibration aspect. This research endeavors to develop an artificial ear specifically designed for bone-conducted occlusion measurements. Finite Element Analysis (FEA) modeling has been employed to gain insights into the behavior of the artificial ear.

Keywords: Artificial ear, bone conducted vibration, occlusion measurement, Finite Element Modeling.

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Authors: Miljan B. Petrović, Dušan B. Petrović, Goran S. Nikolić

Abstract:

This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful.

Keywords: Noise, signal-to-noise ratio, stochastic signals, variance estimation.

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Authors: D. Beziakina, E. Bulgakova

Abstract:

The paper deals with cross-gender and cross-linguistic comparison of pitch characteristics for Tuvinian with two other Turkic languages - Uzbek and Azerbaijani, based on the results of statistical analysis of pitch parameter values and intonation patterns used by male and female speakers.

The main goal of our work is to obtain the ranges of pitch parameter values typical for Tuvinian speakers for the purpose of automatic language identification. We also propose a cross-gender analysis of declarative intonation in the poorly studied Tuvinian language.

The ranges of pitch parameter values were obtained by means of specially developed software that deals with the distribution of pitch values and allows us to obtain statistical language-specific pitch intervals.

Keywords: Speech analysis, Statistical analysis, Speaker recognition, Identification of person.

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

Abstract:

The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: Data mining, Korean linguistic feature, literary fiction, relationship extraction.

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Authors: Nagesha, G. Hemantha Kumar

Abstract:

Although the level crossing concept has been the subject of intensive investigation over the last few years, certain problems of great interest remain unsolved. One of these concern is distribution of threshold levels. This paper presents a new threshold level allocation schemes for level crossing based on nonuniform sampling. Intuitively, it is more reasonable if the information rich regions of the signal are sampled finer and those with sparse information are sampled coarser. To achieve this objective, we propose non-linear quantization functions which dynamically assign the number of quantization levels depending on the importance of the given amplitude range. Two new approaches to determine the importance of the given amplitude segment are presented. The proposed methods are based on exponential and logarithmic functions. Various aspects of proposed techniques are discussed and experimentally validated. Its efficacy is investigated by comparison with uniform sampling.

Keywords: speech signals, sampling, signal reconstruction, asynchronousdelta modulation, non-linear quantization.

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Authors: Anam Rafique, Syed Sohail Ahmed

Abstract:

One of the essential components of much of DSP application is noise cancellation. Changes in real time signals are quite rapid and swift. In noise cancellation, a reference signal which is an approximation of noise signal (that corrupts the original information signal) is obtained and then subtracted from the noise bearing signal to obtain a noise free signal. This approximation of noise signal is obtained through adaptive filters which are self adjusting. As the changes in real time signals are abrupt, this needs adaptive algorithm that converges fast and is stable. Least mean square (LMS) and normalized LMS (NLMS) are two widely used algorithms because of their plainness in calculations and implementation. But their convergence rates are small. Adaptive averaging filters (AFA) are also used because they have high convergence, but they are less stable. This paper provides the comparative study of LMS and Normalized NLMS, AFA and new enhanced average adaptive (Average NLMS-ANLMS) filters for noise cancelling application using speech signals.

Keywords: AFA, ANLMS, LMS, NLMS.

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Authors: Sepideh Fazeli, Fariba Bahrami

Abstract:

Investigating language acquisition is one of the most challenging problems in the area of studying language. Syllable learning as a level of language acquisition has a considerable significance since it plays an important role in language acquisition. Because of impossibility of studying language acquisition directly with children, especially in its developmental phases, computer models will be useful in examining language acquisition. In this paper a computer model of early language learning for syllable learning is proposed. It is guided by a conceptual model of syllable learning which is named Directions Into Velocities of Articulators model (DIVA). The computer model uses simple associational and reinforcement learning rules within neural network architecture which are inspired by neuroscience. Our simulation results verify the ability of the proposed computer model in producing phonemes during babbling and early speech. Also, it provides a framework for examining the neural basis of language learning and communication disorders.

Keywords: Brain modeling, computer models, language acquisition, reinforcement learning.

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Authors: Ali Ganoun

Abstract:

This paper proposes evaluation of sound parameterization methods in recognizing some spoken Arabic words, namely digits from zero to nine. Each isolated spoken word is represented by a single template based on a specific recognition feature, and the recognition is based on the Euclidean distance from those templates. The performance analysis of recognition is based on four parameterization features: the Burg Spectrum Analysis, the Walsh Spectrum Analysis, the Thomson Multitaper Spectrum Analysis and the Mel Frequency Cepstral Coefficients (MFCC) features. The main aim of this paper was to compare, analyze, and discuss the outcomes of spoken Arabic digits recognition systems based on the selected recognition features. The results acqired confirm that the use of MFCC features is a very promising method in recognizing Spoken Arabic digits.

Keywords: Speech Recognition, Spectrum Analysis, Burg Spectrum, Walsh Spectrum Analysis, Thomson Multitaper Spectrum, MFCC.

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Authors: Stefanie Rukavina, Sascha Gruss, Steffen Walter, Holger Hoffmann, Harald C. Traue

Abstract:

OPEN_EmoRec_II is an open multimodal corpus with experimentally induced emotions. In the first half of the experiment, emotions were induced with standardized picture material and in the second half during a human-computer interaction (HCI), realized with a wizard-of-oz design. The induced emotions are based on the dimensional theory of emotions (valence, arousal and dominance). These emotional sequences - recorded with multimodal data (facial reactions, speech, audio and physiological reactions) during a naturalistic-like HCI-environment one can improve classification methods on a multimodal level. This database is the result of an HCI-experiment, for which 30 subjects in total agreed to a publication of their data including the video material for research purposes*. The now available open corpus contains sensory signal of: video, audio, physiology (SCL, respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus Major) and facial reactions annotations.

Keywords: Open multimodal emotion corpus, annotated labels.

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