Vowel Onset Point Research Articles

Stop Consonant-Short Vowel (SCSV) Classification for Tamil Speech Utterances

Tamil Language is one of the ancient Dravidian languages spoken in south India. Most of the Indian languages are syllabic in nature and syllables are in the form of Consonant-Vowel (CV) units. In Tamil language, CV pattern occurs in the beginning, middle and end of a word. In this work, CV units formed with Stop Consonant – Short Vowel (SCSV) were considered for classification task. The work carried out in three stages, Vowel Onset Point (VOP) detection, CV segmentation and classification. VOP is an event at which the consonant part ends and vowel part begins. VOPs are identified using linear Prediction residuals which provide significant characteristics of the excitation source. To segment the CV units, fixed length spectral frames before and after VOPs are considered. Both production based features - Linear Predictive Cepstral Coefficients (LPCC) and perception based features - Mel Frequency Cepstral Coefficients (MFCC) are extracted and given as input to the classifiers designed with multilayer perceptron and support vector machine. A speech corpus of 200 Tamil words uttered by 15 native speakers was used, which covers all SCSV units formed with Tamil stop consonants (/k/,/ch/,/d/,/t/,/p/) and short vowels (/a/,/i/, /u/, /e/, /o/). The classifiers are trained and tested for its performance using various measures. The results indicate that the model built with MFCC using support vector machine RBF kernel outperforms.

RECOGNITION OF TAMIL SYLLABLES USING VOWEL ONSET POINTS WITH PRODUCTION, PERCEPTION BASED FEATURES

Tamil Language is one of the ancient Dravidian languages spoken in south India. Most of the Indian languages are syllabic in nature and syllables are in the form of Consonant-Vowel (CV) units. In Tamil language, CV pattern occurs in the beginning, middle and end of a word. In this work, CV Units formed with Stop Consonant - Short Vowel (SCSV) were considered for classification task. The work carried out in three stages, Vowel Onset Point (VOP) detection, CV segmentation and classification. VOP is an event at which the consonant part ends and vowel part begins. VOPs are identified using linear prediction residuals which provide significant characteristics of the excitation source. To segment the CV units, fixed length spectral frames before and after VOPs are considered. In this work, production based features, Linear Predictive Cepstral Coefficients (LPCC) and perception based features, Perceptual Linear Predictive Cepstral Coefficients (PLP) and Mel Frequency Cepstral Coefficients (MFCC) are extracted which are used to build the SCSV classifier using multilayer perceptron and support vector machine. A speech corpus of 200 Tamil words uttered by 15 native speakers was used, which covers all SCSV units formed with Tamil stop consonants (/k/, /ch/, /d/, /t/, /p/) and short vowels (/a/, /i/, /u/, /e/, /o/). The classifiers are trained and tested for its performance using predictive accuracy measure. The results indicate that perception based features, MFCC and PLP provides better results than production based features, LPCC and the model built using support vector machine outperforms.

Prosodic Mapping Using Neural Networks for Emotion Conversion in Hindi Language

An emotion is made of several components such as physiological changes in the body, subjective feelings and expressive behaviors. These changes in speech signal are mainly observed in prosody parameters such as pitch, duration and energy. Hindi language is mostly syllabic in nature. Syllables are the most suitable basic units for the analysis and synthesis of speech. Therefore, vowel onset point detection method is used to segment the speech utterance into syllable like units. In this work, prosody parameters are modified using instants of significant excitation (epochs) and these instants are detected using zero frequency filtering-based method. Epoch locations in the voiced speech correspond to instants of glottal closure, and in the unvoiced region, they correspond to some random instants of significant excitation. Anger, happiness and sadness emotions are considered as target emotions in the proposed emotion conversion framework. Feedforward neural network models are explored for mapping the prosodic parameters between neutral and target emotions. Predicted prosodic parameters of the target emotion are incorporated into neutral speech at syllable level to produce the desired emotional speech. After incorporating the emotion-specific prosody, perceptual quality of the transformed speech is evaluated by subjective tests.

Automatic detection of breathy voiced vowels in Gujarati speech

This paper proposes a method for automatic detection of breathy voiced vowels in continuous Gujarati speech. As breathy voice is a specific phonetic feature predominantly present in Gujarati among Indian languages, it can be used for identifying Gujarati language. The objective of this paper is to differentiate breathy voiced vowels from modal voiced vowels based on loudness measure. Excitation source characteristics represented by loudness measure are used for differentiating the voice quality. In the proposed method, initially vowel regions in continuous speech are determined by using the knowledge of vowel onset points and epochs. Later, hypothesized vowel segments are classified by using loudness measure. Performance of the proposed method is evaluated on Gujarati speech utterances containing around 47 breathy and 192 modal vowels spoken by 5 male and 5 female speakers. Classification of vowels into breathy or modal voice is achieved with an accuracy of around 94 %.

Detection of Vowel Offset Point From Speech Signal

Vowel regions play important role in various speech tasks, such as speech segmentation, speaker-verification, prosody modification and emotion conversion. The instants at which the onset and offset of vowel take place in the speech signal are known as vowel onset point and vowel offset point, respectively. Vowel regions start with the vowel onset point and end with the vowel offset point. In this letter, we have proposed two methods for determining the vowel offset points from the speech signal. The first method explores the combination of evidences from excitation source, spectral peaks and modulation spectrum for determining the vowel offset point. In the second method, spectral energy within glottal closure region is used for determining the vowel offset point. The proposed vowel offset point detection methods are evaluated on TIMIT database under clean and noisy environments.

Non-uniform time scale modification using instants of significant excitation and vowel onset points

In this paper, a non-uniform time scale modification (TSM) method is proposed for increasing or decreasing speech rate. The proposed method modifies the durations of vowel and pause segments by different modification factors. Vowel segments are modified by factors based on their identities, and pause segments by uniform factors based on the desired speaking rate. Consonant and transition (consonant-to-vowel) segments are not modified in the proposed TSM. These modification factors are derived from the analysis of slow and fast speech collected from professional radio artists. In the proposed TSM method, vowel onset points (VOPs) are used to mark the consonant, transition and vowel regions, and instants of significant excitation (ISE) are used to perform TSM as required. The VOPs indicate the instants at which the onsets of vowels take place. The ISE, also known as epochs, indicate the instants of glottal closure during voiced speech, and some random excitations such as burst onset during non-voiced speech. In this work, VOPs are determined using multiple sources of evidence from excitation source, spectral peaks, modulation spectrum and uniformity in epoch intervals. The ISEs are determined using a zero-frequency filter method. The performance of the proposed non-uniform TSM scheme is compared with uniform and existing non-uniform TSM schemes using epoch and time domain pitch synchronous overlap and add (TD-PSOLA) methods.

Significance of Vowel Onset Point Information for Speaker Verification

This work demonstrates the significance of information about vowel onset points (VOPs) for speaker verification. VOP is defined as the instant at which the onset of vowel takes place. Vowel-like regions can be identified using VOPs. By production, vowel-like regions have impulse-like excitation and therefore impulse-response of vocal tract system is better manifested in them, and are relatively high signal to noise ratio (SNR) regions. Speaker information extracted from such regions may therefore be more discriminative. Due to this better speaker modeling and reliable testing may be possible using the features extracted from vowel-like regions. It is demonstrated in this work that for clean and matched conditions, relatively less number of frames from vowel-like regions are sufficient for speaker modeling and testing. Alternatively, for degraded and mismatched conditions, vowel-like regions provide better performance.

Improved speaker identification in wireless environment

The increasing use of wireless mobile systems is creating great deal of interest in the development of robust speech systems in wireless environment. The major degradations involved in wireless environment are: acoustic environment, speech coding and transmission errors. In this paper, we address the problem of Speaker Identification (SI) from coded and cellular speech. Since speaker-specific characteristics are preserved in steady vowel segments of speech even after coding, the features extracted from these steady vowel regions are used to build the SI system. We have proposed a method to determine the steady vowel region from the speech signal by using vowel onset points and epochs. SI studies are carried out using cellular, coded and microphone speech databases. Autoassociative Neural Network (AANN) models are explored for developing the SI models. Speech coders considered in this work are GSM CELP, and MELP. Significant improvement in the performance of SI system is observed by using proposed approach.

Homomorphic Filtered Spectral Peaks Energy for Automatic Detection of Vowel Onset Point in Continuous Speech

Homomorphic Filtered Spectral Peaks Energy for Automatic Detection of Vowel Onset Point in Continuous Speech

Speaker identification under background noise using features extracted from steady vowel regions

SUMMARYIn this paper, we are exploring features extracted from steady vowel segments for improving the performance of speaker identification system under background noise. Steady vowel regions are produced by periodic impulse‐like excitation and they contain relatively high signal energy. Hence, speaker specific information present in steady vowel regions may be less affected by the noise. In this work, steady vowel regions are determined by using the knowledge of accurate vowel onset points and epochs. Speaker identification studies are carried out using TIMIT database for white and vehicle noises. Universal background model–Gaussian mixture model‐based modeling is explored for developing speaker models. Significant improvement in the performance of speaker identification is observed by using features extracted from steady vowel region in presence of noisy environments. Copyright © 2012 John Wiley & Sons, Ltd.

Vowel onset point detection for noisy speech using spectral energy at formant frequencies

In this paper, we propose a method for robust detection of the vowel onset points (VOPs) from noisy speech. The proposed VOP detection method exploits the spectral energy at formant frequencies of the speech segments present in glottal closure region. In this work, formants are extracted by using group delay function, and glottal closure instants are extracted by using zero frequency filter based method. Performance of the proposed VOP detection method is compared with the existing method, which uses the combination of evidence from excitation source, spectral peaks energy and modulation spectrum. Speech data from TIMIT database and noise samples from NOISEX database are used for analyzing the performance of the VOP detection methods. Significant improvement in the performance of VOP detection is observed by using proposed method compared to existing method.

Vowel Onset Point Detection for Low Bit Rate Coded Speech

In this paper, we propose a method for detecting the vowel onset points (VOPs) for low bit rate coded speech. VOP is the instant at which the onset of the vowel takes place in the speech signal. VOP plays an important role for the applications, such as consonant-vowel (CV) unit recognition and speech rate modification. The proposed VOP detection method is based on the spectral energy present in the glottal closure region of the speech signal. Speech coders considered to carry out this study are Global System for Mobile Communications (GSM) full rate, code-excited linear prediction (CELP), and mixed-excitation linear prediction (MELP). TIMIT database and CV units collected from the broadcast news corpus are used for evaluation. Performance of the proposed method is compared with existing methods, which uses the combination of evidence from the excitation source, spectral peaks energy, and modulation spectrum. The proposed VOP detection method has shown significant improvement in the performance compared to the existing method under clean as well as coded cases. The effectiveness of the proposed VOP detection method is analyzed in CV recognition by using VOP as an anchor point.

Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features

In this work, spectral features extracted from sub-syllabic regions and pitch synchronous analysis are proposed for speech emotion recognition. Linear prediction cepstral coefficients, mel frequency cepstral coefficients and the features extracted from high amplitude regions of spectrum are used to represent emotion specific spectral information. These features are extracted from consonant, vowel and transition regions of each syllable to study the contribution of these regions toward recognition of emotions. Consonant, vowel and the transition regions are determined using vowel onset points. Spectral features extracted from each pitch cycle, are also used to recognize emotions present in speech. The emotions used in this study are: anger, fear, happy, neutral and sad. The emotion recognition performance using sub-syllabic speech segments are compared with the results of conventional block processing approach, where entire speech signal is processed frame by frame. The proposed emotion specific features are evaluated using simulated emotion speech corpus, IITKGP-SESC (Indian Institute of Technology, KharaGPur-Simulated Emotion Speech Corpus). The emotion recognition results obtained using IITKGP-SESC are compared with the results of Berlin emotion speech corpus. Emotion recognition systems are developed using Gaussian mixture models and auto-associative neural networks. The purpose of this study is to explore sub-syllabic regions to identify the emotions embedded in a speech signal, and if possible, to avoid processing of entire speech signal for emotion recognition without serious compromise in the performance.

Spotting and Recognition of Consonant-Vowel Units from Continuous Speech Using Accurate Detection of Vowel Onset Points

In this paper, we propose an efficient approach to spotting and recognition of consonant-vowel (CV) units from continuous speech using accurate detection of vowel onset points (VOPs). Existing methods for VOP detection suffer from lack of high accuracy, spurious VOPs, and missed VOPs. The proposed VOP detection is designed to overcome most of the shortcomings of the existing methods and provide accurate detection of VOPs for improving the performance of spotting and recognition of CV units. The proposed method for VOP detection is carried out in two levels. At the first level, VOPs are detected by combining the complementary evidence from excitation source, spectral peaks, and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in the vowel regions. The spotted CV units are recognized using a two-stage CV recognizer. Two-stage CV recognition system consists of hidden Markov models (HMMs) at the first stage for recognizing the vowel category of a CV unit and support vector machines (SVMs) for recognizing the consonant category of a CV unit at the second stage. Performance of spotting and recognition of CV units from continuous speech is evaluated using Telugu broadcast news speech corpus.

청각 주파수 응답에 기반한 자동 모음 개시 지점 탐지

This paper presents a vowel onset point (VOP) detection method based on the human auditory system. This method maps the perceptual frequency scale, i.e. Mel scale onto a linear acoustic frequency, and then establishes a series of Triangular Mel-weighted Filter Bank simulate the function of band pass filtering in human ear. This nonlinear critical-band filter bank helps greatly reduce the data dimensionality, and eliminate the effect of harmonic waves to make the formants more prominent in the nonlinear spaced Mel spectrum. The sum of mel spectrum peaks energy is extracted as feature for each frame, and the instinct at which the energy amplitude starts rising sharply is detected as VOP, by convolving with Gabor window. For the single-word database which contains 12 vowels articulated with different kinds of consonants, the experimental results showed a good average detection rate of 72.73%, higher than other vowel detection methods based on short-time energy and zero-crossing rate.

Improved vowel onset point detection using epoch intervals

Vowel onset point (VOP) is the instant at which the onset of vowel takes place in the speech signal. Accurate detection of VOP is useful for applications such as consonant–vowel (CV) unit recognition and speech rate modification. Existing VOP detection methods determine VOPs within 40ms deviation, which may not be suitable for the applications mentioned above. In this paper, a two level approach using multiple sources of evidence is proposed for the accurate detection of VOP. In the proposed method, at the first level, VOPs are identified by combining the complementary evidence from excitation source, spectral peaks and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in vowel region. Zero frequency filter method is used to determine the epoch locations in speech. Performance of the proposed method is analyzed using TIMIT database, and compared with the recent method which uses the combination of evidence from excitation source, spectral peaks and modulation spectrum. Using the proposed method about 85% of VOPs are detected within 10ms deviation.

Improved consonant–vowel recognition for low bit‐rate coded speech

SUMMARYIn this paper, we proposed a method for improving the recognition performance of 145 prominent consonant–vowel (CV) units in Indian languages for low bit‐rate coded speech. Proposed CV recognition method is carried out in two levels to reduce the similarity among a large number of CV classes. In the first level, vowel category of CV unit will be recognized, and in the second level, consonant category will be recognized. At each level of the proposed method, complementary evidences from support vector machine and hidden Markov models are combined to enhance the recognition performance. Effectiveness of the proposed two‐level CV recognition method is demonstrated by performing the recognition of isolated CV units and CV units collected from the Telugu broadcast news database. In this work, vowel onset point (VOP) is used as an anchor point for extracting accurate features from the CV unit. Therefore, a method is proposed for accurate detection of VOP in clean and coded speech. The proposed VOP detection method is based on the spectral energy in 500–2500 Hz frequency band of the speech segments present in the glottal closure region. Speech coders considered in this work are GSM full rate (ETSI 06.10), CELP (FS‐1016), and MELP (TI 2.4 kbps). Significant improvement in CV recognition performance is achieved using the proposed two‐level method compared with the existing methods under both clean and coded conditions. Copyright © 2011 John Wiley & Sons, Ltd.

Duration modification using glottal closure instants and vowel onset points

This paper proposes a method for duration (time scale) modification using glottal closure instants (GCI, also known as instants of significant excitation) and vowel onset points (VOP). In general, most of the time scale modification methods attempt to vary the duration of speech segments uniformly over all regions. But it is observed that consonant regions and transition regions between a consonant and the following vowel, and between two consonant regions do not vary appreciably with speaking rate. The proposed method implements the duration modification without changing the durations of the transition and consonant regions. Vowel onset points are used to identify the transition and consonant regions. A VOP is the instant at which the onset of the vowel takes place, which corresponds to the transition from a consonant to the following vowel in most cases. The VOPs are computed using the Hilbert envelope of linear prediction (LP) residual. The instants of significant excitation correspond to the instants of glottal closure (epochs) in the case of voiced speech, and to some random excitations, like the onset of burst, in the case of nonvoiced speech. Manipulation of duration is achieved by modifying the duration of the LP residual with the help of instants of significant excitation as pitch markers. The modified residual is used to excite the time-varying filter whose parameters are derived from the original speech signal. Perceptual quality of the synthesized speech is found to be natural. Performance of the proposed method is compared with the method, where the duration of speech is modified uniformly over all regions. Samples of speech signals for different modification factors is available for listening at http://sit.iitkgp.ernet.in/~ksrao/result.html.

Vowel Onset Point Detection Using Source, Spectral Peaks, and Modulation Spectrum Energies

Vowel onset point (VOP) is the instant at which the onset of vowel takes place during speech production. There are significant changes occurring in the energies of excitation source, spectral peaks, and modulation spectrum at the VOP. This paper demonstrates the independent use of each of these three energies in detecting the VOPs. Since each of these energies represents a different aspect of speech production, it may be possible that they contain complementary information about the VOP. The individual evidences are therefore combined for detecting the VOPs. The error rates measured as the ratio of missing and spurious to the total number of VOPs evaluated on the sentences taken from the TIMIT database are 6.92%, 8.8%, 6.13%, and 4.0% for source, spectral peaks, modulation spectrum, and combined information, respectively. The performance of the combined method for VOP detection is improved by 2.13% compared to the best performing individual VOP detection method.

Extraction and representation of prosodic features for language and speaker recognition

In this paper, we propose a new approach for extracting and representing prosodic features directly from the speech signal. We hypothesize that prosody is linked to linguistic units such as syllables, and it is manifested in terms of changes in measurable parameters such as fundamental frequency ( F 0 ), duration and energy. In this work, syllable-like unit is chosen as the basic unit for representing the prosodic characteristics. Approximate segmentation of continuous speech into syllable-like units is obtained by locating the vowel onset points (VOP) automatically. The knowledge of the VOPs serve as reference for extracting prosodic features from the speech signal. Quantitative parameters are used to represent F 0 and energy contour in each region between two consecutive VOPs. Prosodic features extracted using this approach may be useful in applications such as recognition of language or speaker, where explicit phoneme/syllable boundaries are not easily available. The effectiveness of the derived prosodic features for language and speaker recognition is evaluated in the case of NIST language recognition evaluation 2003 and the extended data task of NIST speaker recognition evaluation 2003, respectively.