Automatic voice onset time estimation from reassignment spectra

Authors:
Veronique Stouten;Hugo Van hamme
Affiliations:
ESAT Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, P.O. 2441, B-3001 Leuven, Belgium;ESAT Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, P.O. 2441, B-3001 Leuven, Belgium
Venue:
Speech Communication
Year:
2009

Citing 4
Cited 1

Spectral features of plosives in connected-speech signals

International Journal of Man-Machine Studies - Special issue on knowledge-based co-operation
Improving the readability of time-frequency and time-scalerepresentations by the reassignment method

IEEE Transactions on Signal Processing
Multitaper Time-Frequency Reassignment for Nonstationary Spectrum Estimation and Chirp Enhancement

IEEE Transactions on Signal Processing
The zero-frequency problem: estimating the probabilities of novel events in adaptive text compression

IEEE Transactions on Information Theory

Automatic voice onset time detection for unvoiced stops (/p/,/t/,/k/) with application to accent classification

Speech Communication

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Abstract

We describe an algorithm to automatically estimate the voice onset time (VOT) of plosives. The VOT is the time delay between the burst onset and the start of periodicity when it is followed by a voiced sound. Since the VOT is affected by factors like place of articulation and voicing it can be used for inference of these factors. The algorithm uses the reassignment spectrum of the speech signal, a high resolution time-frequency representation which simplifies the detection of the acoustic events in a plosive. The performance of our algorithm is evaluated on a subset of the TIMIT database by comparison with manual VOT measurements. On average, the difference is smaller than 10ms for 76.1% and smaller than 20ms for 91.4% of the plosive segments. We also provide analysis statistics of the VOT of /b/, /d/, /g/, /p/, /t/ and /k/ and experimentally verify some sources of variability. Finally, to illustrate possible applications, we integrate the automatic VOT estimates as an additional feature in an HMM-based speech recognition system and show a small but statistically significant improvement in phone recognition rate.