Recognition of syllables in a continuous stream of speech by parcor parameters of linear predictive vocoder

Abstract

Linear predictive coding (LPC) has been used to compress and encode speech signals for digital transmission at a low bit rate. PARCOR parameter associated with LPC that represents a vocal tract model based on a lattice filter structure is considered for speech recognition. The use of FIR coefficients and the frequency response of AR model were previously investigated. This paper reports a method to detect syllables from a continuous stream of speech. The system being developed slides a time window of 20 ms and calculates the PARCOR parameters continuously, feeding them to a syllable classifier. The syllable classifier is a supervised classifier that requires training. The training uses TIMIT speech database, which contains the recordings of 630 speakers of 8 major dialects of American English. The voiced/unvoiced switch built into the LPC vocoder was modified to segment words included in the speech records. Preliminary results of classification are presented in the paper