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Accent Classification Using Support Vector Machines

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2 Author(s)
Pedersen, C. ; Univ. of Queensland, Brisbane ; Diederich, J.

Accent is the pattern of pronunciation and acoustic features in speech which can identify a person's linguistic, social or cultural background. It is an important source of inter-speaker variability, and a particular problem for automated speech recognition. Current approaches to the identification of speaker accent may require specialised linguistic knowledge or analysis of the particular speech contrasts, and often extensive pre-processing on large amounts of data. An accent classification system using time-based segments consisting of Mel Frequency Cepstral Coefficients as features and employing Support Vector Machines is studied for a small corpus of two accents of English. On one- to four-second audio samples from three topics, accuracy in the binary classification task is up to 75% to 97.5%, with very high recall and precision. Its use with mis-matched content is at best 85% with a tendency towards majority-class classification if the accent groups are significantly imbalanced.

Published in:

Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on

Date of Conference:

11-13 July 2007