Search results for: speech enhancement.

Authors: T. Kishore Kumar

Abstract:

Speech enhancement is the process of eliminating noise and increasing the quality of a speech signal, which is contaminated with other kinds of distortions. This paper is on developing an optimum cascaded system for speech enhancement. This aim is attained without diminishing any relevant speech information and without much computational and time complexity. LMS algorithm, Spectral Subtraction and Kalman filter have been deployed as the main de-noising algorithms in this work. Since these algorithms suffer from respective shortcomings, this work has been undertaken to design cascaded systems in different combinations and the evaluation of such cascades by qualitative (listening) and quantitative (SNR) tests.

Keywords: LMS, Kalman filter, Speech Enhancement and Spectral Subtraction.

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Authors: Christian Arcos, Marley Vellasco, Abraham Alcaim

Abstract:

In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario.

Keywords: Binary labels, local binary patterns, mask, wavelet coefficients, speech enhancement, speech recognition.

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Authors: Pogula Rakesh, T. Kishore Kumar

Abstract:

Speech enhancement is a long standing problem with numerous applications like teleconferencing, VoIP, hearing aids and speech recognition. The motivation behind this research work is to obtain a clean speech signal of higher quality by applying the optimal noise cancellation technique. Real-time adaptive filtering algorithms seem to be the best candidate among all categories of the speech enhancement methods. In this paper, we propose a speech enhancement method based on Recursive Least Squares (RLS) adaptive filter of speech signals. Experiments were performed on noisy data which was prepared by adding AWGN, Babble and Pink noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR levels. We then compare the noise cancellation performance of proposed RLS algorithm with existing NLMS algorithm in terms of Mean Squared Error (MSE), Signal to Noise ratio (SNR) and SNR Loss. Based on the performance evaluation, the proposed RLS algorithm was found to be a better optimal noise cancellation technique for speech signals.

Keywords: Adaptive filter, Adaptive Noise Canceller, Mean Squared Error, Noise reduction, NLMS, RLS, SNR, SNR Loss.

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Authors: R. Rishma Rodrigo, R. Radhika, M. Vanitha Lakshmi

Abstract:

Numerous signal processing based speech enhancement systems have been proposed to improve intelligibility in the presence of noise. Traditionally, studies of neural vowel encoding have focused on the representation of formants (peaks in vowel spectra) in the discharge patterns of the population of auditory-nerve (AN) fibers. A method is presented for recording high-frequency speech components into a low-frequency region, to increase audibility for hearing loss listeners. The purpose of the paper is to enhance the formant of the speech based on the Kaiser window. The pitch and formant of the signal is based on the auto correlation, zero crossing and magnitude difference function. The formant enhancement stage aims to restore the representation of formants at the level of the midbrain. A MATLAB software’s are used for the implementation of the system with low complexity is developed.

Keywords: Formant estimation, formant enhancement, pitch detection, speech analysis.

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Authors: C. Ganesh Babu, P. T. Vanathi

Abstract:

In this paper we present an enhanced noise reduction method for robust speech recognition using Adaptive Gain Equalizer with Non linear Spectral Subtraction. In Adaptive Gain Equalizer method (AGE), the input signal is divided into a number of subbands that are individually weighed in time domain, in accordance to the short time Signal-to-Noise Ratio (SNR) in each subband estimation at every time instant. Instead of focusing on suppression the noise on speech enhancement is focused. When analysis was done under various noise conditions for speech recognition, it was found that Adaptive Gain Equalizer method algorithm has an obvious failing point for a SNR of -5 dB, with inadequate levels of noise suppression for SNR less than this point. This work proposes the implementation of AGE when coupled with Non linear Spectral Subtraction (AGE-NSS) for robust speech recognition. The experimental result shows that out AGE-NSS performs the AGE when SNR drops below -5db level.

Keywords: Adaptive Gain Equalizer, Non Linear Spectral Subtraction, Speech Enhancement, and Speech Recognition.

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Authors: Eng. Alaa K. Satti Salih

Abstract:

Revolutions Applications such as telecommunications, hands-free communications, recording, etc. which need at least one microphone, the signal is usually infected by noise and echo. The important application is the speech enhancement, which is done to remove suppressed noises and echoes taken by a microphone, beside preferred speech. Accordingly, the microphone signal has to be cleaned using digital signal processing DSP tools before it is played out, transmitted, or stored. Engineers have so far tried different approaches to improving the speech by get back the desired speech signal from the noisy observations. Especially Mobile communication, so in this paper will do reconstruction of the speech signal, observed in additive background noise, using the Kalman filter technique to estimate the parameters of the Autoregressive Process (AR) in the state space model and the output speech signal obtained by the MATLAB. The accurate estimation by Kalman filter on speech would enhance and reduce the noise then compare and discuss the results between actual values and estimated values which produce the reconstructed signals.

Keywords: Autoregressive Process, Kalman filter, Matlab and Noise speech.

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Authors: Harish Chander, Balwinder Singh, Ravinder Khanna

Abstract:

In this paper, we present the comparative subjective analysis of Improved Minima Controlled Recursive Averaging (IMCRA) Algorithm, the Kalman filter and the cascading of IMCRA and Kalman filter algorithms. Performance of speech enhancement algorithms can be predicted in two different ways. One is the objective method of evaluation in which the speech quality parameters are predicted computationally. The second is a subjective listening test in which the processed speech signal is subjected to the listeners who judge the quality of speech on certain parameters. The comparative objective evaluation of these algorithms was analyzed in terms of Global SNR, Segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) by the authors and it was reported that with cascaded algorithms there is a substantial increase in objective parameters. Since subjective evaluation is the real test to judge the quality of speech enhancement algorithms, the authenticity of superiority of cascaded algorithms over individual IMCRA and Kalman algorithms is tested through subjective analysis in this paper. The results of subjective listening tests have confirmed that the cascaded algorithms perform better under all types of noise conditions.

Keywords: Speech enhancement, spectral domain, time domain, PESQ, subjective analysis, objective analysis.

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Authors: Suman Senapati, Goutam Saha

Abstract:

This work presents a fusion of Log Gabor Wavelet (LGW) and Maximum a Posteriori (MAP) estimator as a speech enhancement tool for acoustical background noise reduction. The probability density function (pdf) of the speech spectral amplitude is approximated by a Generalized Laplacian Distribution (GLD). Compared to earlier estimators the proposed method estimates the underlying statistical model more accurately by appropriately choosing the model parameters of GLD. Experimental results show that the proposed estimator yields a higher improvement in Segmental Signal-to-Noise Ratio (S-SNR) and lower Log-Spectral Distortion (LSD) in two different noisy environments compared to other estimators.

Keywords: Speech Enhancement, Generalized Laplacian Distribution, Log Gabor Wavelet, Bayesian MAP Marginal Estimator.

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

Abstract:

In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to enhance the speech signal from the noisy speech. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the proposed LMS adaptive noise reduction algorithm reduces Mean square Error (MSE) and Log Spectral Distance (LSD) as compared to that of the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed algorithm increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR improvement (ΔSNRseg) values; improves the Mean Opinion Score (MOS) as compared to that of the various existing LMS adaptive noise reduction algorithms. From these experimental results, it is observed that the proposed LMS adaptive noise reduction algorithm reduces the speech distortion and residual noise as compared to that of the existing methods.

Keywords: LMS, speech enhancement, speech quality, residual noise.

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Authors: Asmaa Amehraye, Dominique Pastor, Ahmed Tamtaoui, Driss Aboutajdine

Abstract:

A new analysis of perceptual speech enhancement is presented. It focuses on the fact that if only noise above the masking threshold is filtered, then noise below the masking threshold, but above the absolute threshold of hearing, can become audible after the masker filtering. This particular drawback of some perceptual filters, hereafter called the maskee-to-audible-noise (MAN) phenomenon, favours the emergence of isolated tonals that increase musical noise. Two filtering techniques that avoid or correct the MAN phenomenon are proposed to effectively suppress background noise without introducing much distortion. Experimental results, including objective and subjective measurements, show that these techniques improve the enhanced speech quality and the gain they bring emphasizes the importance of the MAN phenomenon.

Keywords: Perceptual speech filtering, maskee to audible noise, distorsion, musical noise.

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Authors: Talbi Mourad, Salhi Lotfi, Chérif Adnen

Abstract:

In this work, we are interested in developing a speech denoising tool by using a discrete wavelet packet transform (DWPT). This speech denoising tool will be employed for applications of recognition, coding and synthesis. For noise reduction, instead of applying the classical thresholding technique, some wavelet packet nodes are set to zero and the others are thresholded. To estimate the non stationary noise level, we employ the spectral entropy. A comparison of our proposed technique to classical denoising methods based on thresholding and spectral subtraction is made in order to evaluate our approach. The experimental implementation uses speech signals corrupted by two sorts of noise, white and Volvo noises. The obtained results from listening tests show that our proposed technique is better than spectral subtraction. The obtained results from SNR computation show the superiority of our technique when compared to the classical thresholding method using the modified hard thresholding function based on u-law algorithm.

Keywords: Enhancement, spectral subtraction, SNR, discrete wavelet packet transform, spectral entropy Histogram

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Authors: Theologos Athanaselis, Stelios Bakamidis, Ioannis Dologlou

Abstract:

Emotion in speech is an issue that has been attracting the interest of the speech community for many years, both in the context of speech synthesis as well as in automatic speech recognition (ASR). In spite of the remarkable recent progress in Large Vocabulary Recognition (LVR), it is still far behind the ultimate goal of recognising free conversational speech uttered by any speaker in any environment. Current experimental tests prove that using state of the art large vocabulary recognition systems the error rate increases substantially when applied to spontaneous/emotional speech. This paper shows that recognition rate for emotionally coloured speech can be improved by using a language model based on increased representation of emotional utterances.

Keywords: Statistical language model, N-grams, emotionallycoloured speech

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Authors: Zdenek Krnoul

Abstract:

This article investigates a contribution of synthesized visual speech. Synthesis of visual speech expressed by a computer consists in an animation in particular movements of lips. Visual speech is also necessary part of the non-manual component of a sign language. Appropriate methodology is proposed to determine the quality and the accuracy of synthesized visual speech. Proposed methodology is inspected on Czech speech. Hence, this article presents a procedure of recording of speech data in order to set a synthesis system as well as to evaluate synthesized speech. Furthermore, one option of the evaluation process is elaborated in the form of a perceptual test. This test procedure is verified on the measured data with two settings of the synthesis system. The results of the perceptual test are presented as a statistically significant increase of intelligibility evoked by real and synthesized visual speech. Now, the aim is to show one part of evaluation process which leads to more comprehensive evaluation of the sign speech synthesis system.

Keywords: Perception test, Sign speech synthesis, Talking head, Visual speech.

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Authors: K.R. Aida–Zade, C. Ardil, A.M. Sharifova

Abstract:

In this paper, the main principles of text-to-speech synthesis system are presented. Associated problems which arise when developing speech synthesis system are described. Used approaches and their application in the speech synthesis systems for Azerbaijani language are shown.

Keywords: synthesis of Azerbaijani language, morphemes, phonemes, sounds, sentence, speech synthesizer, intonation, accent, pronunciation.

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Authors: Ch.V.Rama Rao, Gowthami., Harsha., Rajkumar., M.B.Rama Murthy, K.Srinivasa Rao, K.AnithaSheela

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This paper presents a new method for estimating the nonstationary noise power spectral density given a noisy signal. The method is based on averaging the noisy speech power spectrum using time and frequency dependent smoothing factors. These factors are adjusted based on signal-presence probability in individual frequency bins. Signal presence is determined by computing the ratio of the noisy speech power spectrum to its local minimum, which is updated continuously by averaging past values of the noisy speech power spectra with a look-ahead factor. This method adapts very quickly to highly non-stationary noise environments. The proposed method achieves significant improvements over a system that uses voice activity detector (VAD) in noise estimation.

Keywords: Noise estimation, Non-stationary noise, Speechenhancement.

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Authors: C. Treerattanaphan, P. Boonpramuk, P. Singla

Abstract:

Frequent, continuous speech training has proven to be a necessary part of a successful speech therapy process, but constraints of traveling time and employment dispensation become key obstacles especially for individuals living in remote areas or for dependent children who have working parents. In order to ameliorate speech difficulties with ample guidance from speech therapists, a website has been developed that supports speech therapy and training for people with articulation disorders in the standard Thai language. This web-based program has the ability to record speech training exercises for each speech trainee. The records will be stored in a database for the speech therapist to investigate, evaluate, compare and keep track of all trainees’ progress in detail. Speech trainees can request live discussions via video conference call when needed. Communication through this web-based program facilitates and reduces training time in comparison to walk-in training or appointments. This type of training also allows people with articulation disorders to practice speech lessons whenever or wherever is convenient for them, which can lead to a more regular training processes.

Keywords: Web-Based Remote Training Program, Thai Speech Therapy, Articulation Disorders.

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Authors: Hai Quang Hong Dam, Hai Ho, Minh Hoang Le Ngo

Abstract:

This paper investigates the problem of blind speech separation from the speech mixture of two speakers. A voice activity detector employing the Steered Response Power - Phase Transform (SRP-PHAT) is presented for detecting the activity information of speech sources and then the desired speech signals are extracted from the speech mixture by using an optimal beamformer. For evaluation, the algorithm effectiveness, a simulation using real speech recordings had been performed in a double-talk situation where two speakers are active all the time. Evaluations show that the proposed blind speech separation algorithm offers a good interference suppression level whilst maintaining a low distortion level of the desired signal.

Keywords: Blind speech separation, voice activity detector, SRP-PHAT, optimal beamformer.

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Authors: Wajdi Ghezaiel, Amel Ben Slimane Rahmouni, Ezzedine Ben Braiek

Abstract:

Many applications of speech communication and speaker identification suffer from the problem of co-channel speech. This paper deals with a multi-resolution dyadic wavelet transform method for usable segments of co-channel speech detection that could be processed by a speaker identification system. Evaluation of this method is performed on TIMIT database referring to the Target to Interferer Ratio measure. Co-channel speech is constructed by mixing all possible gender speakers. Results do not show much difference for different mixtures. For the overall mixtures 95.76% of usable speech is correctly detected with false alarms of 29.65%.

Keywords: Co-channel speech, usable speech, multi-resolutionanalysis, speaker identification

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Authors: Driss Guerchi, Fatiha Djebbar

Abstract:

In this work we introduce an efficient method to limit the impact of the hiding process on the quality of the cover speech. Vector quantization of the speech spectral information reduces drastically the number of the secret speech parameters to be embedded in the cover signal. Compared to scalar hiding, vector quantization hiding technique provides a stego signal that is indistinguishable from the cover speech. The objective and subjective performance measures reveal that the current hiding technique attracts no suspicion about the presence of the secret message in the stego speech, while being able to recover an intelligible copy of the secret message at the receiver side.

Keywords: Speech steganography, LSF vector quantization, fast Fourier transform

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Authors: Sangjun Park, Jungpyo Hong, Byung-Ok Kang, Yun-keun Lee, Minsoo Hahn

Abstract:

In this paper, an algorithm for detecting and attenuating puff noises frequently generated under the mobile environment is proposed. As a baseline system, puff detection system is designed based on Gaussian Mixture Model (GMM), and 39th Mel Frequency Cepstral Coefficient (MFCC) is extracted as feature parameters. To improve the detection performance, effective acoustic features for puff detection are proposed. In addition, detected puff intervals are attenuated by high-pass filtering. The speech recognition rate was measured for evaluation and confusion matrix and ROC curve are used to confirm the validity of the proposed system.

Keywords: Gaussian mixture model, puff detection and cancellation, speech enhancement.

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Authors: Min Ah Kang, Sangbae Jeong, Minsoo Hahn

Abstract:

This paper presents the source extraction system which can extract only target signals with constraints on source localization in on-line systems. The proposed system is a kind of methods for enhancing a target signal and suppressing other interference signals. But, the performance of proposed system is superior to any other methods and the extraction of target source is comparatively complete. The method has a beamforming concept and uses an improved time-frequency (TF) mask-based BSS algorithm to separate a target signal from multiple noise sources. The target sources are assumed to be in front and test data was recorded in a reverberant room. The experimental results of the proposed method was evaluated by the PESQ score of real-recording sentences and showed a noticeable speech enhancement.

Keywords: Beam forming, Non-stationary noise reduction, Source separation, TF mask.

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Authors: Panikos Heracleous

Abstract:

In this study, the use of silicon NAM (Non-Audible Murmur) microphone in automatic speech recognition is presented. NAM microphones are special acoustic sensors, which are attached behind the talker-s ear and can capture not only normal (audible) speech, but also very quietly uttered speech (non-audible murmur). As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech conversion etc.) for sound-impaired people. Using a small amount of training data and adaptation approaches, 93.9% word accuracy was achieved for a 20k Japanese vocabulary dictation task. Non-audible murmur recognition in noisy environments is also investigated. In this study, further analysis of the NAM speech has been made using distance measures between hidden Markov model (HMM) pairs. It has been shown the reduced spectral space of NAM speech using a metric distance, however the location of the different phonemes of NAM are similar to the location of the phonemes of normal speech, and the NAM sounds are well discriminated. Promising results in using nonlinear features are also introduced, especially under noisy conditions.

Keywords: Speech recognition, unvoiced speech, nonlinear features, HMM distance measures

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Authors: Safdar Tanweer, Abdul Mobin, Afshar Alam

Abstract:

The performance and analysis of speech recognition system is illustrated in this paper. An approach to recognize the English word corresponding to digit (0-9) spoken by 2 different speakers is captured in noise free environment. For feature extraction, speech Mel frequency cepstral coefficients (MFCC) has been used which gives a set of feature vectors from recorded speech samples. Neural network model is used to enhance the recognition performance. Feed forward neural network with back propagation algorithm model is used. However other speech recognition techniques such as HMM, DTW exist. All experiments are carried out on Matlab.

Keywords: Speech Recognition, MFCC, Neural Network, classifier.

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Authors: Young-Hwan Song, Doo-Heon Kyun, Jong-Kuk Kim, Myung-Jin Bae

Abstract:

People have the habitual pitch level which is used when people say something generally. However this pitch should be changed irregularly in the presence of noise. So it is useful to estimate SNR of speech signal by pitch. In this paper, we obtain the energy of input speech signal and then we detect a stationary region on voiced speech. And we get the pitch period by NAMDF for the stationary region that is not varied pitch rapidly. After getting pitch, each frame is divided by pitch period and the likelihood of closed pitch is estimated. In this paper, we proposed new parameter, NLF, to estimate the SNR of received speech signal. The NLF is derived from the correlation of near pitch periods. The NLF is obtained for each stationary region in voiced speech. Finally we confirmed good performance of the estimation of the SNR of received input speech in the presence of noise.

Keywords: Likelihood, pitch, SNR, speech.

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Authors: Zarine Avetisyan

Abstract:

The present paper presents the discussion of scholars concerning speech impact, peculiarities of its realization, speech strategies and techniques in particular. Departing from the viewpoints of many prominent linguists, the paper suggests that manipulative argumentation be viewed as a most pervasive speech strategy with a certain set of techniques which are to be found in modern American political discourse. The precedence of their occurrence allows us to regard them as pragmatic patterns of speech impact realization in effective public speaking.

Keywords: Manipulative argumentation, political discourse, speech impact, technique.

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Authors: M. A. Ben Messaoud, A. Bouzid, N. Ellouze

Abstract:

Speech Segmentation is the measure of the change point detection for partitioning an input speech signal into regions each of which accords to only one speaker. In this paper, we apply two features based on multi-scale product (MP) of the clean speech, namely the spectral centroid of MP, and the zero crossings rate of MP. We focus on multi-scale product analysis as an important tool for segmentation extraction. The MP is based on making the product of the speech wavelet transform coefficients (WTC). We have estimated our method on the Keele database. The results show the effectiveness of our method. It indicates that the two features can find word boundaries, and extracted the segments of the clean speech.

Keywords: Speech segmentation, Multi-scale product, Spectral centroid, Zero crossings rate.

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Authors: Van Nhan Nguyen, Harald Holone

Abstract:

Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this field. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air traffic control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as specific approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed.

Keywords: Automatic Speech Recognition, ASR, Air Traffic Control, ATC.

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Authors: H. B. Kekre, Tanuja K. Sarode

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Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table shows computational complexity of these three algorithms. Here we have introduced a new performance parameter Average Fractional Change in Speech Sample (AFCSS). Our FCG algorithm gives far better performance considering mean absolute error, AFCSS and complexity as compared to others.

Keywords: Vector Quantization, Data Compression, Encoding, , Speech coding.

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Authors: E. Krasnova, E. Bulgakova, V. Shchemelinin

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The paper deals with acoustic-spectrographic voice identification method in terms of its performance in non-native language speech. Performance evaluation is conducted by comparing the result of the analysis of recordings containing native language speech with recordings that contain foreign language speech. Our research is based on Tajik and Russian speech of Tajik native speakers due to the character of the criminal situation with drug trafficking. We propose a pilot experiment that represents a primary attempt enter the field.

Keywords: Speaker identification, acoustic-spectrographic method, non-native speech.

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Authors: Nazaket Gazieva

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Voice biometric data associated with physiological, psychological and other factors are widely used in forensic phonoscopy. There are various methods for identifying and verifying a person by voice. This article explores the minimum speech signal data as individual parameters of a speech signal. Monozygotic twins are believed to be genetically identical. Using the minimum data of the speech signal, we came to the conclusion that the voice imprint of monozygotic twins is individual. According to the conclusion of the experiment, we can conclude that the minimum indicators of the speech signal are more stable and reliable for phonoscopic examinations.

Keywords: Biometric voice prints, fundamental frequency, phonogram, speech signal, temporal characteristics.

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