Speech timer estimator
Furthermore, some methods consider only word-initial stops because the role of VOT in discriminating between voiced and unvoiced stops is more prominent in such occurrences. Methods based on statistical classifiers employ training with high-dimensional feature vectors. Such methods are difficult to employ in a scenario where there is no transcription available. Keshet, “ Automatic measurement of voice onset time using discriminative structured prediction,” J. or to focus the analysis on segments of the signal containing only one stop consonant. Wang, “ Automatic estimation of voice onset time for word-initial stops by applying random forest to onset detection,” J. Kim, “ Automatic voice onset time detection for unvoiced stops (/p/,/t/,/k/) with application to accent classification,” Speech Commun. 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 4. (02)00151-6 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. Ramesh, “ The voicing feature for stop consonants: Recognition experiments with continuously spoken alphabets,” Speech Commun. 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 4.
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as related to aphasia, apraxia, etc.Īutomatic 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. Hannequin, “ Voice onset time in aphasia, apraxia of speech and dysarthria: A review,” Clin. VOT is routinely measured in the context of clinical research studies 7 7. (02)00151-6 that inclusion of VOT as an additional feature can improve the phone recognition rate of an automatic speech recognition system. Van Hamme, “ Automatic voice onset time estimation from reassignment spectra,” Speech Commun. Alwan, “ On the perception of voicing in syllable-initial plosives in noise,” J. It also has applications in psychoacoustic studies 3 3. especially when the stops are in word-initial position. Abramson, “ A cross-language study of voicing in initial stops: Acoustical measurements,” Word 20, 384– 422 (1964).
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It is an important temporal attribute to discriminate between “voiced” and “unvoiced” stops, 2 2.
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Voice-onset time (VOT) is defined as the interval between the onset of the stop-burst and the onset of the laryngeal vibrations succeeding the burst.
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Stevens, Acoustic Phonetics ( MIT Press, Cambridge, MA, 1998), Chaps. A stop consonant comprises multiple sub-phonetic events namely closure, the burst onset, aspiration (if any), and the voice onset (when followed by a voiced phone).