Vowel Onset Point Research Articles

Analyzing changes in parkinsonian speech over time: a diachronic experimental phonetics study.

In this contribution the use of web resources for the longitudinal study of speech rhythm of a 'well-known' person diagnosed with Parkinson's disease, the American actor Alan Alda, is proposed. A corpus of 20 speech samples produced in the period between 1979 and 2021 was collected from the web. A rhythmical analysis was conducted, based on two parameters: the percentage of vocalic portion on the total duration of the utterance (%V) and the VtoV, the mean duration of the interval between two consecutive vowel onset points. The results of this study confirm an early alteration of rhythm in parkinsonian speech, with an abnormal increase of %V, already occurring some years before the clinical diagnosis. The observation of speech rhythm variation can therefore be considered as the basis for the realization of a sustainable and non-invasive procedure in support to early diagnosis of Parkinson's disease.

Variational autoencoder for prosody‐based speaker recognition

AbstractThis paper describes a novel end‐to‐end deep generative model‐based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker‐specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable‐like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency ( ), are then extracted at the syllable level and used to train/adapt a speaker‐dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender‐dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.

Speech Rhythm Variation in Early-Stage Parkinson's Disease: A Study on Different Speaking Tasks.

Patients with Parkinson's disease (PD) usually reveal speech disorders and, among other symptoms, the alteration of speech rhythm. The purpose of this study is twofold: (1) to test the validity of two acoustic parameters—%V, vowel percentage and VtoV, the mean interval between two consecutive vowel onset points—for the identification of rhythm variation in early-stage PD speech and (2) to analyze the effect of PD on speech rhythm in two different speaking tasks: reading passage and monolog. A group of 20 patients with early-stage PD was involved in this study and compared with 20 age- and sex-matched healthy controls (HCs). The results of the acoustic analysis confirmed that %V is a useful cue for early-stage PD speech characterization, having significantly higher values in the production of patients with PD than the values in HC speech. A simple speaking task, such as the reading task, was found to be more effective than spontaneous speech in the detection of rhythmic variations.

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.

An efficient approach for detecting vowel onset and offset points in speech signal

Vowel onset point (VOP) and vowel end point (VEP) are the instants of starting and ending of a vowel, respectively. VOPs and VEPs are equally important for accurate detection of vowels and development of different speech based applications. In a single algorithm, simultaneously detecting VOPs and VEPs is very challenging. In this paper, an efficient approach is proposed for robustly extracting the magnitude dynamics at each time instant of the speech signal. The mean and variance of the magnitude dynamics over an analysis frame happen to be significantly higher for the vowels when compared to other nonvowel, silence and noise regions. In this study, the average magnitude dynamics (AMD) over an analysis frame is used as the front-end feature. The AMD values at each time instant are then nonlinearly mapped (NL-AMD) by using sigmoidal function to sharpen the transitions at the VEPs and suppress the variations in the higher magnitude regions. The NL-AMD is equally discriminative at the VOPs and the VEPs. Consequently, most of the VOPs and the VEPs are detected within a smaller deviation. The experimental evaluations presented in this study show that, for the clean as well as noisy test conditions, the proposed feature outperforms the earlier reported front-end features for the task of detecting the VOPs and the VEPs.

VEP Detection for Read, Extempore and Conversation Speech

ABSTRACT In this paper, we propose a novel approach for accurate detection of the vowel end points (VEPs) in any mode of speech. VEP is the instant at which the vowel ends in the speech signal. In this study, we have considered three broad modes of speech, namely; conversation, extempore, and read. The existing methods were explored the VEP detection for read mode of speech, and it may not be appropriate for the VEP detection in extempore and conversation modes. This is due to the acoustic characteristic of read mode is very different from the modes as mentioned earlier. To handle this problem, we proposed a two-stage method for accurately detecting the VEPs, irrespective of modes. At the first stage, vowel onset points (VOPs) are detected in a speech signal using our recent method based on continuous wavelet transform and phone boundary. VOP represents the start of the vowel in the speech signal. At the second stage, phone boundaries are detected using spectral transition measure approach, and then the closest succeeding phone boundary for each detected VOP is considered as detected VEP. Experiments involve TIMIT and Bengali speech corpora. Performance of the proposed VEP detection method is compared with two state-of-the-art signal processing methods. The significance of the proposed method is shown by automatically detecting vowel regions from the TIMIT and Bengali speech corpora. The evaluation results report that the performance of the proposed method is significantly 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.

Punjabi Speech Syllable Segmentation Using Vowel Onset Point Identification

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Detection of Nasalized Voiced Stops in Cleft Palate Speech Using Epoch-Synchronous Features

The presence of velopharyngeal dysfunction in individuals with cleft palate CP nasalizes the voiced stops. Due to this, voiced stops /b/, /d/, /g/ tend to be perceive like nasal consonants /m/, /n/, /ng/. In this work, a novel algorithm is proposed for the detection of nasalized voiced stops in CP speech using epoch-synchronous features. Speech regions corresponding to consonant and consonant-vowel transitions are segmented using the knowledge of glottal activity, syllable nucleus, low-frequency spectral dominance, and vowel onset point. The segmented regions are epoch-synchronously processed to analyze the spectral, spectro-temporal, excitation source, and periodicity characteristics of normal and nasalized voiced stops. Spectral and spectro temporal features are computed using single pole filter based time-frequency representation. The amplitude of Hilbert envelope of linear prediction residual, measured around the epoch is used to analyze the effect of nasalization on excitation source. Comparison of speech frames of successive inter-epoch intervals is carried out to analyze the periodicity characteristics. The proposed features are used to develop a support vector machine classifier for the classification of normal and nasalized voiced stops. Segmentation accuracy for the proposed knowledge based method is found to be better than the hidden Markov model based force-alignment approach. The detection rate of nasalized voiced stops is found to be high for the proposed epoch synchronous features than the conventional Mel-frequency cepstral coefficients.

Synthesis of Emotional Speech by Prosody Modification of Vowel Segments of Neutral Speech

Speech is viewed as a combination of voiced and unvoiced regions. Voiced speech is produced due to vibration of the vocal cords. The vibrating pattern of vocal cords is different in different emotions. During production of some consonant sound units, vocal cords do not vibrate. Therefore, consonants are less effective for emotion generation in speech signal. In this paper, we have considered only vowel regions for emotion synthesis using three prosody parameters duration, intensity and pitch patterns. Vowel like regions (VLR) is identified using vowel onset and offset points. Onset and offset points are starting and ending points of the vowel like regions. It is observed that during emotional synthesis from neutral speech mainly vowel regions of speech utterance are modified significantly. Our experimental result shows that the emotion synthesis using only prosody modification of VLR is significantly better than emotion synthesis of prosody modification at syllable level and it is also very effective in time consideration. The average mean opinion score is calculated using only vowel level prosody modification. The average mean opinion scores for angry, happy and fear emotional speeches are 3.85, 3.60 and 4.03, respectively. These mean opinion scores are better than syllable level prosody modification which are 3.56, 3.17 and 3.92 for angry, happy and fear emotions, respectively.

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.

Application of non-negative frequency-weighted energy operator for vowel region detection

In this paper, a novel technique has been proposed for the vowel region detection from the continuous speech using an envelope of the derivative of the speech signal, which is a non-negative, frequency-weighted energy operator. The proposed vowel region detection method is implemented using a two-stage algorithm. The first stage of vowel region detection consists of speech signal analysis to detect vowel onset points (VOP) and vowel end-points (VEP) using an instantaneous energy contour obtained from the envelope of the derivative of a speech signal. The VOPs and VEPs are spotted using the peak-finding algorithm based upon the first order Gaussian differentiator. The next stage consists of removal of spurious vowel regions and the correction of hypothesized VOP and VEP locations using combined cues obtained from the uniformity of epoch intervals and strength of the excitation of the speech signal. Performance of the proposed method for detecting vowel regions from the speech signal is evaluated using TIMIT acoustic-phonetic speech corpus. The proposed approach resulted in significantly high detection rate and less false alarm rate compared to the state-of-the-art methods in both clean and noisy environments.

Syllable Segmentation of Tamil Speech Signals Using Vowel Onset Point and Spectral Transition Measure

Segmentation plays vital role in speech recognition systems. An automatic segmentation of Tamil speech into syllable has been carried out using Vowel Onset Point (VOP) and Spectral Transition Measure (STM). VOP is a phonetic event used to identify the beginning point of the vowel in speech signals. Spectral Transition Measure is performed to find the significant spectral changes in speech utterances. The performance of the proposed syllable segmentation method is measured corresponding to manual segmentation and compared with the exiting syllable method using VOP and Vowel Offset Point (VOF). The result of the experiments shows the effectiveness of the proposed system.

Neural network and GMM based feature mappings for consonant–vowel recognition in emotional environment

In this work, we propose a mapping function based feature transformation framework for developing consonant–vowel (CV) recognition system in the emotional environment. An effective way of conveying messages is by expressing emotions during human conversations. The characteristics of CV units differ from one emotion to other emotions. The performance of existing CV recognition systems is degraded in emotional environments. Therefore, we have proposed mapping functions based on artificial neural network and GMM models for increasing the accuracy of CV recognition in the emotional environment. The CV recognition system has been explored to transform emotional features to neutral features using proposed mapping functions at CV and phone levels to minimize mismatch between training and testing environments. Vowel onset and offset points have been used to identify vowel, consonant and transition segments. Transition segments are identified by considering initial 15% speech samples between vowel onset and offset points. The average performance of CV recognition system is increased significantly using feature mapping technique at phone level in three emotional environments (anger, happiness, and sadness).

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.

Consonant-vowel unit recognition using dominant aperiodic and transition region detection

This work reports a method of Consonant-Vowel (CV) unit recognition by detecting the Dominant Aperiodic component Regions (DARs) and by predicting the Duration of Transition Regions (DTRs) in speech. DAR detection is performed using complementary information from source and vocal tract. While source information is extracted using sub-fundamental frequency filtering of speech, vocal tract information is extracted using a) Dominant Resonant Frequency (DRF) and b) High to Low Frequency component Ratio (HLFR), computed from Hilbert envelope of Numerator Group Delay (HNGD) spectrum of zero-time windowed signal. The DTR is predicted by using vocal tract constriction information. Subsequently, detected DARs and predicted DTRs are compared with manually marked regions and finally used for CV unit recognition of Indian languages. Conventionally, CV unit recognition is performed by anchoring the Vowel Onset Point (VOP) and assuming fixed durations for transition and consonant regions on either side of the VOP. However, in speech, the duration of transition and consonantal regions vary depending on the type of consonants and vowels. In the proposed method, the use of dynamic values for consonant duration and transition regions have resulted in better consonant recognition improving CV unit recognition.

Improvements in the Detection of Vowel Onset and Offset Points in a Speech Sequence

Detecting the vowel regions in a given speech signal has been a challenging area of research for a long time. A number of works have been reported over the years to accurately detect the vowel regions and the corresponding vowel onset points (VOPs) and vowel end points (VEPs). Effectiveness of the statistical acoustic modeling techniques and the front-end signal processing approaches has been explored in this regard. The work presented in this paper aims at improving the detection of vowel regions as well as the VOPs and VEPs. A number of statistical modeling approaches developed over the years have been employed in this work for the aforementioned task. To do the same, three-class classifiers (vowel, nonvowel and silence) are developed on the TIMIT database employing the different acoustic modeling techniques and the classification performances are studied. Using any particular three-class classifier, a given speech sample is then forced-aligned against the trained acoustic model under the constraints of first-pass transcription to detect the vowel regions. The correctly detected and spurious vowel regions are analyzed in detail to find the impact of semivowel and nasal sound units on the detection of vowel regions as well as on the determination of VOPs and VEPs. In addition to that, a novel front-end feature extraction technique exploiting the temporal and spectral characteristics of the excitation source information in the speech signal is also proposed. The use of the proposed excitation source feature results in the detection of vowel regions that are quite different from those obtained through the mel-frequency cepstral coefficients. Exploiting those differences in the obtained evidences by using the two kinds of features, a technique to combine the evidences is also proposed in order to get a better estimate of the VOPs and VEPs. When the proposed techniques are evaluated on the vowel---nonvowel classification systems developed using the TIMIT database, significant improvements are noted. Moreover, the improvements are noted to hold across all the acoustic modeling paradigms explored in the presented work.

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.