Speech recognition using automatically derived acoustic baseforms

This paper investigates procedures for obtaining user-configurable speech recognition vocabularies. These procedures use example utterances of vocabulary words to perform unsupervised automatic acoustic baseform determination in terms of a set of speaker independent subword acoustic units. Several procedures, differing both in the definition of subword acoustic model context and in the phonotactic constraints used in decoding have been investigated. The tendency of input utterances to contain out-of-vocabulary or non-speech information is accounted for using likelihood ratio based utterance verification procedures. Comparisons of different definitions of the likelihood ratio used for utterance verification and of different criteria for estimating parameters used in the likelihood ratio test have been performed. The performance of these techniques has been evaluated on utterances taken from a trial of a voice label recognition service