Use of Syntactic Segmentation and Stressed Syllable Location in Phonemic Recognition

Abstract

Automatic speech recognition is expected to be more successful when syntactically related information is incorporated into early stages of recognition. Phonemic decisions, in particular, are expected to be more accurate and less ambiguous when contextual information is considered. A computer program has been developed for detecting boundaries between syntactic constituents from fall-rise fundamental frequency (F0) contours in connected speech. Another program detects some stressed syllables from F0 contours, intensity contours, and timing information. Resulting boundary detections and stressed syllable locations are used by a preliminary method for estimating distinctive features of some phonemes in connected speech. Vowel and consonant recognition is attempted first in the stressed syllables. Other readily detected segments, such as coronal unvoiced strident fricatives, are also found. Detected constituent boundary positions are compared with stored information about boundary positions, and are used both to choose candidate sentences independently and to help clarify phonemic results. Results for a limited sentence-recognition task are reported, and extensions to more versatile sentence recognition from syntactic segmentation, stressed-syllable location, and distinctive features estimation are suggested.