Derivation of the optimal set of phonetic transcriptions for a word from its acoustic realizations
This paper deals with a set of methods developed in order to derive multiple variants of phonetic transcriptions for words, given sample utterances of the words and an inventory of context-dependent sub-word units. These methods use a two phase process to derive the phonetic transcriptions. The first phase consists in generating a set of possible transcriptions for the word using an N-best phonetic decoding of the available utterances of that word. The second phase consists in selecting, in the set of possible transcriptions of the word, the ones that describe the best the word. The selection of the best transcriptions is accomplished according to different criteria. The Frequency criterion chooses the k most frequent transcriptions in the set of all phonetic decoding of that word, while the Maximum Likelihood (ML) criterion chooses the k most likely ones. With the two criteria k is the same whatever the word is, and each of the k variants describes all the training utterances of the word. A partition procedure, which determines the optimal number of transcriptions for each word, is then investigated. This procedure assumes that, in the set of the selected transcriptions, each transcription must describe a subset of the utterances of the word. So, the goal is to find the suitable transcriptions and to associate each of them to a subset of the pronunciations (utterances). Two iterative algorithms are developed and evaluated, and a compromise between the likelihood and the number of elements of the optimal set of transcriptions is studied. Speaker-independent speech recognition experiments showed that the ML criterion outperforms the Frequency criterion and that the performance obtained with the former criterion is comparable to that obtained with reference transcriptions. Moreover, results obtained on different speaker-independent speech recognition tasks showed an improvement in performance when the training set of utterances is partitioned between the selected transcriptions; i.e. when each selected transcription represents only a subset of the utterances.
optimal set phonetic transcriptions acoustic realizations frequency criterion partition n-best decoding maximum likelihood criterion phonotactic constraints