Document Type
Article
Publication Date
2016
College/Unit
College of Education and Human Services
Department/Program/Center
Communication Sciences and Disorders
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.
Digital Commons Citation
Donai, Jeremy J.; Motiian, Saeid; and Doretto, Gianfranco, "Automated Classification of Vowel Category and Speaker Type in the High-Frequency Spectrum" (2016). Faculty & Staff Scholarship. 2346.
https://researchrepository.wvu.edu/faculty_publications/2346
Source Citation
Donai, J. J., Motiian, S., & Doretto, G. (2016). Automated classification of vowel category and speaker type in the high-frequency spectrum. Audiology Research, 6(1). https://doi.org/10.4081/audiores.2016.137
Comments
©Copyright J.J. Donai et al., 2016 Licensee PAGEPress, Italy Audiology Research 2016;6:137