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Conference Papers Year : 2011

Learning vocal tract variables with multi-task kernels

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Abstract

The problem of acoustic-to-articulatory speech inversion continues to be a challenging research problem which sig- nificantly impacts automatic speech recognition robustness and accuracy. This paper presents a multi-task kernel based method aimed at learning Vocal Tract (VT) variables from the Mel-Frequency Cepstral Coefficients (MFCCs). Unlike usual speech inversion techniques based on individual esti- mation of each tract variable, the key idea here is to consider all the target variables simultaneously to take advantage of the relationships among them and then improve learning per- formance. The proposed method is evaluated using synthetic speech dataset and corresponding tract variables created by the TAsk Dynamics Application (TADA) model and com- pared to the hierarchical ε-SVR speech inversion technique.
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Dates and versions

hal-00826050 , version 1 (04-06-2013)

Identifiers

  • HAL Id : hal-00826050 , version 1

Cite

Hachem Kadri, Emmanuel Duflos, Philippe Preux. Learning vocal tract variables with multi-task kernels. 36th International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, Prague, Czech Republic. ⟨hal-00826050⟩
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