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Discriminative training of HMM stream exponents for audio-visual speech recognition

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2 Author(s)
Potamianos, G. ; AT&T Labs., Florham Park, NJ, USA ; Graf, H.P.

We propose the use of discriminative training by means of the generalized probabilistic descent (GPB) algorithm to estimate hidden Markov model (HMM) stream exponents for audio-visual speech recognition. Synchronized audio and visual features are used to respectively train audio-only and visual-only single-stream HMMs of identical topology by maximum likelihood. A two-stream HMM is then obtained by combining the two single-stream HMMs and introducing exponents that weigh the log-likelihood of each stream. We present the GPD algorithm for stream exponent estimation, consider a possible initialization, and apply it to the single speaker connected letters task of the AT&T bimodal database. We demonstrate the superior performance of the resulting multi-stream HMM to the audio-only, visual-only, and audio-visual single-stream HMMs

Published in:

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on  (Volume:6 )

Date of Conference:

12-15 May 1998

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