Abstract

This study examines methods for recognizing native and accented voiceless stops based on voice onset time (VOT). These methods are tested on data from the Tball corpus of early elementary school children, which includes both native English speakers and Spanish speakers learning English, and which is transcribed to highlight pronunciation variation. We examine the English voiceless stop series, which have long VOT and aspiration, and the corresponding voiceless stops in Spanish accented English, which have short VOT and little aspiration. The methods tested are : (1) to train hidden Markov models (HMMs) based on native speech and then extract the VOT times by post‐processing phone‐level alignments, (2) to train HMMs with explicit aspiration models, and (3) to train, for each phoneme, different HMMs for native and accented variants. Error rates of 23%–53% for distinguishing phone VOT …

Date

September 1, 2005

Authors

Abe Kazemzadeh, Sungbok Lee, Shrikanth Narayanan

Journal

The Journal of the Acoustical Society of America

Volume

118

Issue

3_Supplement

Pages

2026-2026

Publisher

Acoustical Society of America