Abstract
This work describes classification of speech from native and non-native speakers, enabling accent-dependent automatic speech recognition. In addition to the acoustic signal, lexical features from transcripts of the speech data can also provide significant evidence of a speaker’s accent type. Subsets of the Fisher corpus, ranging over diverse accents, were used for these experiments. Relative to human-audited judgments, accent classifiers that exploited acoustic and lexical features achieved up to 84.5% classification accuracy. Compared to a system trained only on native speakers, using this classifier in a recognizer with accent-specific acoustic and language models resulted in 16.5% improvement for the non-native speakers, and a 7.2% improvement overall.
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© 2006 Springer-Verlag Berlin Heidelberg
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Faria, A. (2006). Accent Classification for Speech Recognition. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_25
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DOI: https://doi.org/10.1007/11677482_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32549-9
Online ISBN: 978-3-540-32550-5
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