Inference of variable-length acoustic units for continuous speech recognition

Abstract

In the field of speech recognition, the patterns assumed to structure the speech material (phonemes, triphones, words...) are defined a priori according to a linguistic criterion, whereas the recognition criterion is based on an acoustic similarity measure. From this may result a lack of consistency for the recognition units. We explore the possibility of a more data-driven approach, where recognition units are derived according to an acoustic criterion, and then, mapped to variable length sequences of phonemes in an unsupervised way. Continuous speech recognition experiments are reported to evaluate the consistency of those units as opposed to linguistically defined units.