Coarse classification using a hierarchical decision tree and top down parsing

In this paper, we describe a robust technique for segmenting an utterance into a sequence of coarse phonetic classes. The resulting coarse class string is used to provide contextual information for further phonetic analysis, and in lexical access to limit the number of word candidates. Each 10 ms interval of the utterance is first given a probability of belonging to each of five classes: silence, vowel, nasal-like, strong fricative and weak fricative. The probabilities are assigned using a hierarchical classification scheme with Gaussian classifiers at each node. A fuzzy C-means clustering procedure is used to learn the class means and variances from unlabeled data. Dynamic programming is used to align the utterance with all possible coarse class strings in the lexicon. The performance of the classifier has been evaluated on the TI speaker independent isolated digits data. The correct word is hypothesized by a coarse class sequence more than 99 percent of the time.