Title :
Dialect/Accent Classification Using Unrestricted Audio
Author :
Huang, Rongqing ; Hansen, John H L ; Angkititrakul, Pongtep
Author_Institution :
Dept. of Electr. Eng., Texas Univ., Richardson, TX
Abstract :
This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the word level rather than making a single decision at the utterance level. The basic WDC algorithm also provides options for further modeling and decision strategy improvement. Two sets of classifiers are employed for WDC: a word classifier DW(k) and an utterance classifier D u. DW(k) is boosted via the AdaBoost algorithm directly in the probability space instead of the traditional feature space. Du is boosted via the dialect dependency information of the words. For a small training corpus, it is difficult to obtain a robust statistical model for each word and each dialect. Therefore, a context adapted training (CAT) algorithm is formulated, which adapts the universal phoneme Gaussian mixture models (GMMs) to dialect-dependent word hidden Markov models (HMMs) via linear regression. Three separate dialect corpora are used in the evaluations that include the Wall Street Journal (American and British English), NATO N4 (British, Canadian, Dutch, and German accent English), and IViE (eight British dialects). Significant improvement in dialect classification is achieved for all corpora tested
Keywords :
Gaussian processes; hidden Markov models; natural language processing; regression analysis; speech recognition; AdaBoost algorithm; English; Gaussian mixture models; accent classification; context adapted training algorithm; decision strategy; dialect-dependent word hidden Markov models; large vocabulary continuous speech recognition; linear regression; text-independent decision problem; unrestricted audio; word-based dialect classification; word-based modeling technique; Automatic speech recognition; Computational efficiency; Context modeling; Hidden Markov models; Linear regression; Natural languages; Probability; Robustness; Speech recognition; Vocabulary; Accent/dialect classification; AdaBoost algorithm; context adapted trianing; dialect dependency information; limited training data; robust acoustic modeling; word-based modeling;
Journal_Title :
Audio, Speech, and Language Processing, IEEE Transactions on
DOI :
10.1109/TASL.2006.881695