Automated Classification of Vowel Category and Speaker Type in the High-Frequency Spectrum.

Abstract

The high-frequency region of vowel signals (above the third formant or F3) has received little research attention. Recent evidence, however, has documented the perceptual utility of high-frequency information in the speech signal above the traditional frequency bandwidth known to contain important cues for speech and speaker recognition. The purpose of this study was to determine if high-pass filtered vowels could be separated by vowel category and speaker type in a supervised learning framework. Mel frequency cepstral coefficients (MFCCs) were extracted from productions of six vowel categories produced by two male, two female, and two child speakers. Results revealed that the filtered vowels were well separated by vowel category and speaker type using MFCCs from the high-frequency spectrum. This demonstrates the presence of useful information for automated classification from the high-frequency region and is the first study to report findings of this nature in a supervised learning framework.