The voicing feature for stop consonants: recognition experiments with continuously spoken alphabets

Niyogi, Partha; Ramesh, P.

doi:10.1016/s0167-6393(02)00151-6

Cited by 23 publications

(14 citation statements)

References 12 publications

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“…It also has applications in psychoacoustic studies 3 and accent identification. 4 It is shown in previous studies 5,6 that inclusion of VOT as an additional feature can improve the phone recognition rate of an automatic speech recognition system. VOT is routinely measured in the context of clinical research studies 7 as related to aphasia, apraxia, etc.…”

Section: Motivationmentioning

confidence: 99%

“…Automatic measurement of VOT is required to reduce the human labor involved in manual measurements and for applications such as automatic speech recognition and accent identification. Methods for the measurement of VOT fall into two categories: (a) those which explicitly identify the locations of the burst and voicing onsets through a set of customized acoustic-phonetic rules (knowledge-based), 4,6 and (b) those which train a learning machine (such as random forest, support vector machine) to estimate the VOT using some acoustic features corresponding to the stop-to-voiced-phone transition event. 8,9 Many of the high performing methods require phonetic transcription either to identify the segment of the speech signal containing the stop consonant through forced-alignment 4,9 or to focus the analysis on segments of the signal containing only one stop consonant.…”

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of voice-onset time in continuous speech using temporal measures

Prathosh

Ramakrishnan

Ananthapadmanabha³

2014

The Journal of the Acoustical Society of America

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This paper proposes an automatic acoustic-phonetic method for estimating voice-onset time of stops. This method requires neither transcription of the utterance nor training of a classifier. It makes use of the plosion index for the automatic detection of burst onsets of stops. Having detected the burst onset, the onset of the voicing following the burst is detected using the epochal information and a temporal measure named the maximum weighted inner product. For validation, several experiments are carried out on the entire TIMIT database and two of the CMU Arctic corpora. The performance of the proposed method compares well with three state-of-the-art techniques.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Estimation of voice-onset time in continuous speech using temporal measures

Prathosh

Ramakrishnan

Ananthapadmanabha³

2014

The Journal of the Acoustical Society of America

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“…The effect of such division of models itself was not presented. Niyogi and Ramesh (2003) employed the voicing onset time in a two-pass HMM-based speech recognition system to reclassify the segments recognised as stop consonants.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Incorporating the voicing information into HMM-based automatic speech recognition in noisy environments

Jančovič

Köküer

2009

Speech Communication

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In this paper, we propose a model for the incorporation of voicing information into a speech recognition system in noisy environments. The employed voicing information is estimated by a novel method that can provide this information for each filterbank channel and does not require information about the fundamental frequency.The voicing information is modelled by employing the Bernoulli distribution. The voicing model is obtained for each HMM state and mixture by a Viterbi-style training procedure. The proposed voicing incorporation is evaluated both within a standard model and two other models that had compensated for the noise effect, the missing-feature and the multi-conditional training model. Experiments are first performed on noisy speech data from the Aurora 2 database. Significant performance improvements are achieved when the voicing information is incorporated within the standard model as well as the noise-compensated models. The employment of voicing information is also demonstrated on a phoneme recognition task on the noise-corrupted TIMIT database and considerable improvements are observed.

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“…The VOT research has been successfully applied cross-linguistically (e.g., Keating, 1984;Ladefoged and Maddieson, 1996;Shimizu, 1990;Poon and Mateer, 1985;Dixit and Brown, 1985). VOT has also been used in automatic speech recognition to improve the identification rate of stops (Niyogi and Ramesh, 2003).…”

Section: Introductionmentioning

confidence: 99%

Corpus-Based Investigations on the Phonetics of Consonant Voicing

Möbius¹

2004

Flin

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Within and across languages the realization of consonant voicing is highly variable. This study aims to identify, and quantify, the segmental, prosodic and positional factors that have an influence on consonant voicing. A widely used acoustic measure of voicing, viz. voice onset time, is known to have disadvantages both in a cross-linguistic framework, where it fails to provide sufficient Information for certain stop consonant classifications, and across consonant classes because it is not defined for fricatives and sonorants. This study applies the voicing profile method to the analysis of voicing properties of consonants in German. The voicing profile is defined äs the frame-by-frame voicing Status of Speech sound realizations in a Speech corpus. The speech database was judiciously constructed to cover systematically all possible Speech sound combinations in German and a number of positional and prosodic contexts in which these combinations occur. The results are put in a cross-linguistic perspective by comparing the voicing profiles of Gerrnan stops to those of stops in three other languages, viz. Mandarin Chinese, Hindi, and Mexican Spanish. The results are also discussed in the context of the production and maintenance of voicing during speech production. The voicing profile analysis is intended to serve äs a methodology for investigating the discrepancies between the phonemic voicing specification of a speech sound and its phonetic realization in connected speech.

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The voicing feature for stop consonants: recognition experiments with continuously spoken alphabets

Cited by 23 publications

References 12 publications

Estimation of voice-onset time in continuous speech using temporal measures

Estimation of voice-onset time in continuous speech using temporal measures

Incorporating the voicing information into HMM-based automatic speech recognition in noisy environments

Corpus-Based Investigations on the Phonetics of Consonant Voicing

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