Neural network acoustic and visual speech recognition system training method and apparatus

Abstract

The apparatus for the recognition of speech includes an acoustic preprocessor, a visual preprocessor, and a speech classifier that operates on the acoustic and visual preprocessed data. The acoustic preprocessor comprises a log mel spectrum analyzer that produces an equal mel bandwidth log power spectrum. The visual processor detects the motion of a set of fiducial markers on the speaker's face and extracts a set of normalized distance vectors describing lip and mouth movement. The speech classifier uses a multilevel time-delay neural network operating on the preprocessed acoustic and visual data to form an output probability distribution that indicates the probability of each candidate utterance having been spoken, based on the acoustic and visual data. The training system includes the speech recognition apparatus and a control processor with an associated memory. Noisy acoustic input training data together with visual data is used to generate acoustic and visual feature training vectors for processing by the speech classifier. A control computer adjusts the synaptic weights of the speech classifier based upon the noisy input training data and exemplar output vectors for producing a robustly trained classifier based on the analogous visual counterpart of the Lombard effect.