Vowel Onset Point Detection Research Articles

VOP detection for read and conversation speech using CWT coefficients and phone boundaries

In this paper, we propose a novel approach for accurate detection of vowel onset points (VOPs). A VOP is the instant at which a vowel begins in a speech signal. Precise identification of VOPs is important for various speech applications such as speech segmentation and speech rate modification. Existing methods detect the majority of VOPs to an accuracy of 40 ms deviation, which may not be appropriate for the above speech applications. To address this issue, we proposed a two-stage approach for accurate detection of VOPs. At the first stage, VOPs are detected using continuous wavelet transform coefficients, and the position of the detected VOPs are corrected using phone boundaries in the second stage. The phone boundaries are detected by the spectral transition measure method. Experiments are done using TIMIT and Bengali speech corpora. Performance of the proposed approach is compared with two standard signal processing based methods as well as with a recent VOP detection technique. The evaluation results show that the proposed method performs better than the existing methods.

Vowel Onset Point Based Screening of Misarticulated Stops in Cleft Lip and Palate Speech

The presence of velopharyngeal dysfunction, dental occlusion, and mislearned articulation in individuals with cleft lip and palate (CLP) results in the production of misarticulated stop consonants. The present work considers vowel onset points (VOPs) as the anchor points, around which the consonant-vowel (CV) transition regions are segmented to analyze the difference between normal and misarticulated stops. VOPs are located using an epoch-synchronously computed feature called maximum weighted inner product. Spectro-temporal dynamics of CV transitions anchored around VOP are analyzed using two-dimensional discrete cosine transform (2D-DCT) coefficients, where 2D-DCT coefficients are derived from single pole filter (SPF) based time-frequency representation. The SPF-based 2D-DCT coefficients are used to train a support vector machine for the classification of normal and misarticulated stops, where the class of misarticulated stops includes weak, nasalized, palatal, velar, pharyngeal, glottal, and devoicing errors produced by CLP speakers. The performance of the proposed VOP detection algorithm is evaluated on a database containing CV units of normal and misarticulated stops, and the results are compared with the state-of-the-art VOP detection methods. The classification results obtained for the proposed SPF-based 2D-DCT coefficients are compared with the short-time Fourier transform-based 2D-DCT coefficients and Mel-frequency cepstral coefficients. Further, the performance of the proposed system is compared with the hidden Markov model-based goodness of pronunciation approach.

Automatic syllabification of speech signal using short time energy and vowel onset points

This paper describes a language independent method for automatic syllabification of speech signal. This method utilizes the valleys in short time energy (STE) contour and location of vowel onset points (VOP) for marking the syllable boundaries. In the proposed method, automatic syllabification is performed in three steps. First, long silence/pause regions are marked with the help of speech/non-speech detection. Then VOPs are located from the Hilbert Envelope of LP residual. The existence of more than one VOP in a continuous speech region (identified using speech/non-speech detection in the first step) is an indication of syllable boundaries within the region. Location with minimum energy in the STE contour between two consecutive VOP is identified as the syllable boundary. Since automatic VOP detection algorithm fails to detect some of the VOPs, certain syllable boundaries will be missed. Therefore, at the third step, additional syllable boundaries are detected from STE contour by fixing a valley threshold which is equal to the mean value of STE corresponding to each speech region between two consecutive syllable boundaries. This method is evaluated for 50 sentences each in read, extempore and conversational mode speech of Malayalam and Bengali languages. Overall accuracy of 80% is obtained with ± 50 ms tolerance with reference to manually marked syllable boundaries for this database. Method also shows good accuracy in case of TIMIT and NTIMIT data without tuning of thresholds and other parameters. This method is useful for applications that do not require exact syllable boundaries, rather a meaningful separation of syllables. Application of this technique for prosody based emotion recognition is illustrated using Emo-DB German emotional database.

Detection of vowel onset and offset points using non‐local similarity between DWT approximation coefficients

In a given speech signal, transition at the vowel offset points (VEPs) is quite different when compared to the vowel onset points (VOPs). Consequently, most of the features reported for the detection of VOPs fail to detect VEPs. To address this issue, a front-end speech parametrisation approach is proposed for simultaneously detecting VOPs and VEPs. In the proposed approach, first energy due to the high-frequency unvoiced sound units is suppressed by using discrete wavelet transform (DWT). Then, weight values (WVs) are assigned to each of the sample points by computing similarity between the analysis frames within a search neighbourhood using non-local means (NLM) estimation. The WVs computed from the NLM are significantly less when the frames under consideration are similar in comparison to the dissimilar ones. Since vowels are longer regions and exhibit periodicity, there will be more similarity in the case of frames belonging to these regions. In this work, this aspect of the WVs is used as a feature for detecting VOPs and VEPs. The proposed feature is observed to outperform the earlier reported features for the task of detecting VOPs as well as VEPs. The improvement observed in the detection accuracy of VEPs is significant.

A pre-processing method for improvement of vowel onset point detection under noisy conditions

Vowel onset point (VOP) is the instant of time at which vowel region starts in a speech signal. VOP plays a vital role in different applications of speech processing, such as syllable detection, speaker verification, duration modification, language identification etc. There are different existing algorithms for the detection of instance of VOP in a speech signal. The algorithm based on the combined evidences extracted from the source excitation, spectral peaks and modulation spectrum has been used as a baseline system for the present work. The baseline system performs well under clean speech data. However, under noisy conditions the performance of the baseline system degrades. The performance of the system degrades in terms of more number of spurious VOPs, which get detected under noisy speech conditions. According to the available literature, this degraded performance is due to the spectral broadening of the speech in the noisy environments. In this paper we have proposed a pre-processing technique on top of the baseline system to reduce this spectral broadening effect of noise. The noisy speech data are passed through the pre-processing algorithm in order to minimize the spectral broadening effect of speech signal. The pre-processed speech is then passed through the baseline system to detect the VOPs in the speech signal. Experiments were carried out under clean and different noisy speech signals. The results of the experiment show an improvement of 16–21% in terms of removal of spurious VOPs, over the existing baseline system under different noisy speech conditions. Further, the performance of the proposed method has been compared with two different best performing techniques for detection of VOP, and found that the proposed method gives a superior level of performance in terms of identification accuracy and identification rate.

Robust analysis for improvement of vowel onset point detection under noisy conditions

Vowel onset point (VOP) is the instant of time at which the vowel region starts in a speech signal. The VOPs are used as anchor points to design various speech based systems. Different algorithms exist in the literature to identify the occurrences of vowels in continuous spoken utterances. The algorithm based on combined evidences derived from source excitation, spectral peaks and modulation spectrum have been used as a baseline system for the present study. The baseline system provides a satisfactory level of performance under clean data condition. However under noisy data condition the performance of the previous system may be improved further by additional pre-processing of the raw speech data and post-processing the detected VOPs. In this paper we propose to use the speech enhancement techniques as pre-processing module to remove the noise from the speech data under different noisy conditions. The pre-processed speech data is then passed through the baseline system to detect the VOPs. It has been observed that there exist several spurious VOPs at the output of the baseline system. We propose to use a post-processing module based on average signal-to-noise ratio and information derived from the glottal closure instant to remove the spurious VOPs. The experiments were carried out on clean, artificially injected noisy, and data collected from the practical noisy environments. The results suggest that the proposed system using pre-processing and post-processing modules is robust and shows an improvement of 28–35 % over the existing baseline system by removing the spurious VOPs under different noisy conditions.

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.

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

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.

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.

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.

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.