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

We propose the use of discriminative training by means of the generalized probabilistic descent (GPD) 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.