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• W1537449681 abstract "Tone error is commonly observed in tonal language acquisition. Correct tone production is especially challenging for native speakers of non-tonal languages. In this paper, we exploit the fundamental frequency variation (FFV) feature for Mandarin tone error detection. We propose to use FFV through two approaches: (1) Concatenating FFVs along side with standard speech recognition features; (2) Token FFV: Characterizing pitch variation with longer temporal context through GMM tokenization and n-gram language modeling. Our results show that tone error detection improves by incorporating FFV features and the two approaches are complementary to each other." @default.
• W1537449681 created "2016-06-24" @default.
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• W1537449681 date "2015-04-01" @default.
• W1537449681 modified "2023-09-24" @default.
• W1537449681 title "Tokenizing fundamental frequency variation for Mandarin tone error detection" @default.
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• W1537449681 doi "https://doi.org/10.1109/icassp.2015.7178995" @default.
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