Abstract
Presents a framework for designing a hidden Markov model (HMM)-based audio-visual automatic speech recognition system based on minimum classification error (MCE) training. Audio/visual HMMs are optimized with MCE training based on the generalized probabilistic descent (GPD) method, and their likelihoods are combined using model-dependent stream weights which are also estimated with the GPD method. Experimental results of speaker-independent isolated word recognition show that the GPD optimization of the audio/visual HMMs and the use of GPD-based model-dependent stream weights provide a significant improvement in system performance, leading to a 47%-81% error reduction over a conventional system which consists of HMMs trained based on the maximum likelihood criterion and globally-tied stream weights estimated with the GPD method.
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