Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The effect of listener accent background on accent perception and comprehension
EURASIP Journal on Audio, Speech, and Music Processing
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
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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.