Analysis of nasal consonants using perceptual linear prediction.

Until recently, speech analysis techniques have been built around the all-pole linear predictive model. This study examines the effectiveness of using the perceptual linear predictive method for analyzing nasal consonants. Six speakers (three men and three women) produced 300 CV syllables with initial nasal consonants /m/ and /n/. A threshold-based boundary detection algorithm was developed to extract nasal segments from the CV contexts. Poles of a fifth-order perceptual linear predictive model were calculated and the frequency of the second pole was used to characterize the place of articulation of nasal consonants. Results indicated that the frequency for the second transformed pole was significantly lower for /m/ than for /n/ and was independent of factors such as a vowel context and gender of the speaker. A nasal identification rate of 86% was obtained based on the frequency of the second pole. The use of the perceptual linear predictive method may thus overcome some difficulties associated with analyzing nasal consonants.