초록 ▼

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 spe

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.

대표청구항 ▼

A training system for a speech recognition system comprising: (a) a speech recognition system for recognizing utterances belonging to a pre-established set of allowable candidate utterances using acoustic speech signals and selected concomitant dynamic visual facial feature motion between selected f

A training system for a speech recognition system comprising: (a) a speech recognition system for recognizing utterances belonging to a pre-established set of allowable candidate utterances using acoustic speech signals and selected concomitant dynamic visual facial feature motion between selected facial features associated with acoustic speech generation, comprising, (i) an acoustic feature extraction apparatus for converting signals representative of dynamic acoustic speech into a corresponding dynamic acoustic feature vector set of signals, (ii) a dynamic visual feature extraction apparatus for converting signals representative of the selected concomitant dynamic facial feature motion associated with acoustic speech generation into a corresponding dynamic visual feature vector set of signals, and (iii) a time delay neural network classifying apparatus with an input-to-output transfer characteristic controlled by a set of adjustable synaptic weights for generating an output response vector representing a conditional probability distribution of the allowable candidate speech utterances by accepting and operating on a set of corresponding time-delayed dynamic acoustic and visual feature vector pairs that are respectively supplied by the acoustic and visual feature extraction apparatus to a set of inputs; and (b) a control system comprising a control processor and an associated memory coupled to the speech recognition system for initializing parameters, for controlling the speech recognition system, for storing acoustic and visual output exemplar vectors, for computing output errors, and for adjusting the time delay neural network classifying apparatus synaptic weights based on the computed errors in accordance with a prescribed training procedure.