Abstract
This paper presents the design and evaluation of a speaker-independent audio-visual speech recognition (AVSR) system that utilizes a segment-based modeling strategy. The audio and visual feature streams are integrated using a segment-constrained hidden Markov model, which allows the visual classifier to process visual frames with a constrained amount of asynchrony relative to proposed acoustic segments. The core experiments in this paper investigate several different visual model structures, each of which provides a different means for defining the units of the visual classifier and the synchrony constraints between the audio and visual streams. Word recognition experiments are conducted on the AV-TIMIT corpus under variable additive noise conditions. Over varying acoustic signal-to-noise ratios, word error rate reductions between 14% and 60% are observed when integrating the visual information into the automatic speech recognition process.
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