Linear Predictive Coding (LPC) has been used to compress and encode speech signals for digital transmission at a low bit rate. The Partial Correlation (PARCOR) parameter associated with LPC that represents a vocal tract model based on a lattice filter structure is considered for speech recognition. For the same purpose, the use of FIR coefficients and the frequency response of AR model were previously investigated. <p>In this thesis, we investigate the mechanics of the speech production process in human beings and discuss the place and manner of articulation for each of the major phoneme classes of American English. Then we characterize some typical vowel and consonant phonemes by using the eighth order PARCOR parameter associated with LPC.<p>This thesis explores a method to detect phonemes from a continuous stream of speech. The system being developed slides a time window of 16 ms and calculates PARCOR parameters continuously, feeding them to a phoneme classifier. The phoneme 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 training data are grouped into the vowel group including phoneme [ae], [iy] and [uw] and the consonant group including [sh] and [f]. After the training, the decision rule is derived. We design two classifiers in this thesis, one is a vowel classifier and the other one is a consonant classifier, both of them use the maximum likelihood decision rule to classify unknown phonemes. <p>The results of classification of vowel and consonant in a one-syllable word are shown in the thesis. The correct classification rate is 65:22% for the vowel group. The correct classification rate is 93:51% for the consonant group. The results indicate that PARCOR parameters have the potential capability to characterize the phoneme.

Linear Predictive Coding (LPC) has been used to compress and encode speech signals for digital transmission at a low bit rate. The Partial Correlation (PARCOR) parameter associated with LPC that represents a vocal tract model based on a lattice filter structure is considered for speech recognition. For the same purpose, the use of FIR coefficients and the frequency response of AR model were previously investigated. <p>In this thesis, we investigate the mechanics of the speech production process in human beings and discuss the place and manner of articulation for each of the major phoneme classes of American English. Then we characterize some typical vowel and consonant phonemes by using the eighth order PARCOR parameter associated with LPC.<p>This thesis explores a method to detect phonemes from a continuous stream of speech. The system being developed slides a time window of 16 ms and calculates PARCOR parameters continuously, feeding them to a phoneme classifier. The phoneme 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 training data are grouped into the vowel group including phoneme [ae], [iy] and [uw] and the consonant group including [sh] and [f]. After the training, the decision rule is derived. We design two classifiers in this thesis, one is a vowel classifier and the other one is a consonant classifier, both of them use the maximum likelihood decision rule to classify unknown phonemes. <p>The results of classification of vowel and consonant in a one-syllable word are shown in the thesis. The correct classification rate is 65:22% for the vowel group. The correct classification rate is 93:51% for the consonant group. The results indicate that PARCOR parameters have the potential capability to characterize the phoneme.