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- W2123162330 abstract "This paper presents a new approach to estimate “universal” phoneme posterior probabilities for mixed language speech recognition. More specifically, we propose a new theoretical framework to combine phoneme class posterior probabilities in a principled way by using (statistical) evidence about the language identity. We investigate the proposed approach in a mixed language environment (Speech-Dat(II)) consisting of five European languages. Our studies show that the proposed approach can yield significant improvements on a mixed language task, while maintaining the performance on monolingual tasks. Additionally, through a case study, we also demonstrate the potential benefits of the proposed approach for non-native speech recognition." @default.
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- W2123162330 date "2011-05-01" @default.
- W2123162330 modified "2023-09-25" @default.
- W2123162330 title "Language dependent universal phoneme posterior estimation for mixed language speech recognition" @default.
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- W2123162330 doi "https://doi.org/10.1109/icassp.2011.5947482" @default.
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