Automatic derivation of multiple variants of phonetic transcriptions from acoustic signals

This paper deals with two methods for automatically finding multiple phonetic transcriptions of words, given sample utterances of the words and an inventory of context-dependent subword units. The two approaches investigated are based on an analysis of theN -best phonetic decoding of the available utterances. In the set of transcriptions resulting from theN -best decoding of all the utterances, the first method selects theK most frequent variants (Frequency Criterion) , while the second method selects the K most likely ones (Maximum Likelihood Criterion). Experiments carried out on speaker-independent recognition showed that the performance obtained with the ”Maximum Likelihood Criterion” is not much different from that obtained with manual transcriptions. In the case of speaker-dependent speech recognition, the estimate of the 3 most likely transcription variants of each word, yields promising results.