Glottal Closure Regions Research Articles

Cascade convolutional neural network‐long short‐term memory recurrent neural networks for automatic tonal and nontonal preclassification‐based Indian language identification

AbstractThis work presents an automatic tonal/nontonal preclassification‐based Indian language identification (LID) system. Languages are firstly classified into tonal and nontonal categories, and then, individual languages are identified from the languages of the respective categories. This work proposes the use of pitch Chroma and formant features for this task, and also investigates how Mel‐frequency Cepstral Coefficients (MFCCs) complement these features. It further explores block processing (BP), pitch synchronous analysis (PSA)‐ and glottal closure regions (GCRs)‐based approaches for feature extraction, using syllables as basic units. Cascade convolutional neural network (CNN)‐long short‐term memory (LSTM) model using syllable‐level features has been developed. National Institute of Technology Silchar language database (NITS‐LD) and OGI‐Multilingual Telephone Speech Corpus (OGI‐MLTS) have been used for experimental validation. The proposed system based on the score combination of Cascade CNN‐LSTM models of Chroma (extracted from BP method), first two formants and MFCCs (both extracted from GCR method) reports the highest accuracies. In the preclassification stage, the observed accuracies are 91%, 87.3%, and 85.1% for NITS‐LD, for 30 s, 10 s, and 3 s test data respectively. For OGI‐MLTS database, the respective accuracies are 86.7%, 83.1%, and 80.6%. That amounts to absolute improvements of 11.6%, 12.3%, and 13.9% for NITS‐LD, and 12.5%, 11.9%, and 12.6% for OGI‐MLTS database with respect to that of the baseline system. The proposed preclassification‐based LID system shows improvements of 7.3%, 6.4%, and 7.4% for NITS‐LD and 6.1%, 6.7%, and 7.2% for OGI‐MLTS database over the baseline system for the three respective test data conditions.

Improved vowel region detection from a continuous speech using post processing of vowel onset points and vowel end-points

Vowels are produced with an open configuration of the vocal tract, without any audible friction. The acoustic signal is relatively loud with varying strength of impulse-like excitation. Vowels possess significant energy content in the low-frequency bands of the speech signal. Acoustic events such as vowel onset point (VOP) and vowel end-point (VEP) can be used as landmarks to detect vowel regions in a speech signal. In this paper, a two-stage algorithm is proposed to detect precise vowel regions. In the first level, the speech signal is processed using zero frequency filtering to emphasize energy content in low-frequency bands of speech. Zero frequency filtered signal predominantly contains low-frequency content of the speech signal as it is filtered around 0 Hz. This process is followed by the extraction of dominant spectral peaks from the magnitude spectrum around glottal closure regions of the speech signal. The vowel onset points and vowel end-points are obtained by convolving the enhanced spectral contour of zero frequency filtered signal with first order Gaussian differentiator. In the next level, a post-processing is carried out in the regions around VOP and VEP to remove spurious vowel regions based on uniformity of epoch intervals. In addition, the positions of VOPs and VEPs are also corrected using the strength of the excitation of the speech signal. The performance of the proposed vowel region detection method is compared with the existing state of art methods on TIMIT acoustic-phonetic speech corpus. It is reported that this method produced significant improvement in vowel region detection in clean and noisy environments.

Identification of Indian languages using multi-level spectral and prosodic features

In this paper spectral and prosodic features extracted from different levels are explored for analyzing the language specific information present in speech. In this work, spectral features extracted from frames of 20 ms (block processing), individual pitch cycles (pitch synchronous analysis) and glottal closure regions are used for discriminating the languages. Prosodic features extracted from syllable, tri-syllable and multi-word (phrase) levels are proposed in addition to spectral features for capturing the language specific information. In this study, language specific prosody is represented by intonation, rhythm and stress features at syllable and tri-syllable (words) levels, whereas temporal variations in fundamental frequency (F 0 contour), durations of syllables and temporal variations in intensities (energy contour) are used to represent the prosody at multi-word (phrase) level. For analyzing the language specific information in the proposed features, Indian language speech database (IITKGP-MLILSC) is used. Gaussian mixture models are used to capture the language specific information from the proposed features. The evaluation results indicate that language identification performance is improved with combination of features. Performance of proposed features is also analyzed on standard Oregon Graduate Institute Multi-Language Telephone-based Speech (OGI-MLTS) database.

Pitch synchronous and glottal closure based speech analysis for language recognition

This paper explores pitch synchronous and glottal closure (GC) based spectral features for analyzing the language specific information present in speech. For determining pitch cycles (for pitch synchronous analysis) and GC regions, instants of significant excitation (ISE) are used. The ISE correspond to the instants of glottal closure (epochs) in the case of voiced speech, and some random excitations like onset of burst in the case of nonvoiced speech. For analyzing the language specific information in the proposed features, Indian language speech database (IITKGP-MLILSC) is used. Gaussian mixture models are used to capture the language specific information from the proposed features. Proposed pitch synchronous and glottal closure spectral features are evaluated using language recognition studies. The evaluation results indicate that language recognition performance is better with pitch synchronous and GC based spectral features compared to conventional spectral features derived through block processing. GC based spectral features are found to be more robust against degradations due to background noise. Performance of proposed features is also analyzed on standard Oregon Graduate Institute Multi-Language Telephone-based Speech (OGI-MLTS) database.

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.

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 source, system, and prosodic features

In this work, source, system, and prosodic features of speech are explored for characterizing and classifying the underlying emotions. Different speech features contribute in different ways to express the emotions, due to their complementary nature. Linear prediction residual samples chosen around glottal closure regions, and glottal pulse parameters are used to represent excitation source information. Linear prediction cepstral coefficients extracted through simple block processing and pitch synchronous analysis represent the vocal tract information. Global and local prosodic features extracted from gross statistics and temporal dynamics of the sequence of duration, pitch, and energy values represent the prosodic information. Emotion recognition models are developed using above mentioned features separately, and in combination. Simulated Telugu emotion database (IITKGP-SESC) is used to evaluate the proposed features. The emotion recognition results obtained using IITKGP-SESC are compared with the results of internationally known Berlin emotion speech database (Emo-DB). Autoassociative neural networks, Gaussian mixture models, and support vector machines are used to develop emotion recognition systems with source, system, and prosodic features, respectively. Weighted combination of evidence has been used while combining the performance of systems developed using different features. From the results, it is observed that, each of the proposed speech features has contributed toward emotion recognition. The combination of features improved the emotion recognition performance, indicating the complementary nature of the features.

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.