You're not from 'round here, are you?: naive Bayes detection of non-native utterance text
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Automatic speech recognition and speech variability: A review
Speech Communication
Phonetic unification of multiple accents for spanish and arabic languages
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Robust and optimum features for persian accent classification using artificial neural network
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Native vs. non-native accent identification using Japanese spoken telephone numbers
Speech Communication
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The performance of speech recognition systems degrades when speaker accent is different from that in the training set. Accent-independent or accent-dependent recognition both require collection of more training data. In this paper, we propose a faster accent classification approach using phoneme-class models. We also present our findings in acoustic features sensitive to a Cantonese accent, and possibly other Asian language accents. In addition, we show how we can rapidly transform a native accent pronunciation dictionary to that for accented speech by simply using knowledge of the native language of the foreign speaker. The use of this accent-adapted dictionary reduces recognition error rate by 13.5%, similar to the results obtained from a longer, data-driven process.