Nortel Networks Corporation

Bell Canada

Christopher K Toulson

Vishwa N Gupta

Peter R Stubley

Matthew Lennig

David Bruce Peters

Andre Gillet

Patrick J Kenny

Comparing a series of observations representing unknown speech, to stored models representing known speech, the series of observations being divided into at least two blocks each comprising two or more of the observations, is carried out in an order which makes better use of memory. First, the observations in one of the blocks are compared (31), to a subset comprising one or more of the models, to determine a likelihood of a match to each of the one or more models. This step is repeated (33) for models other than those in the subset; and the whole process is repeated (34) for each block.

Method and apparatus for speech recognition

A flexible vocabulary speech recognition system is provided for recognizing speech transmitted via the public switched telephone network. The flexible vocabulary recognition (FVR) system is a phoneme based system. The phonemes are modelled as hidden Markov models. The vocabulary is represented as concatenated phoneme models. The phoneme models are trained using Viterbi training enhanced by: substituting the covariance matrix of given phonemes by others, applying energy level thresholds and voiced, unvoiced, silence labelling constraints during Viterbi training. Specific vocabulary members, such as digits, are represented by allophone models. A* searching of the lexical network is facilitated by providing a reduced network which provides estimate scores used to evaluate the recognition path through the lexical network. Joint recognition and rejection of out-of-vocabulary words are provided by using both cepstrum and LSP parameter vectors.

Phoneme based speech recognition

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Palo Alto, CA, United States of America

Christopher K Toulson