A coarse phonetic knowledge source for template independent large vocabulary word recognition

Abstract
In this paper we present a template independent knowledge source (KS), that uses coarse phonetic information to substantially constrain the candidate vocabulary for use in word hypothesization with very large vocabularies. It consists of three parts: the segmenter that breaks a test utterance up into a sequence of coarse phonetic classes, the knowledge compiler that generates a reference dictionary containing the appropriate coarse phonetic representations for each word candidate and finally, a matching engine. Coarse phonetic classification is performed using linear discriminant analysis, more specifically perceptron classification. The knowledge compiler first generates a phonemic representation and segmental durations by rule from a list of word candidates (i.e., from text), and then derives coarse phonetic class segments. Matching is performed by a nonlinear time alignment algorithm based on dissimilarity scores between detected and lexical coarse class segments. The coarse phonetic KS was tested by compiling a word list of approximately 1500 words. Using only the coarse classes Silence, Plosive, Fricative, Vocalic, Front Vowel, Back Vowel, Nasal and R, a vocabulary reduction to 5% of the original vocabulary is achieved at lower than 5% error rate for three different speakers.

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