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- W1622032071 abstract "The paper proposes a general version of a coupled Hidden Markov/Bayesian Network model for performing phoneme recognition on acoustic-articulatory data. The model uses knowledge learned from the articulatory measurements, available for training, for phoneme recognition on the acoustic input. After training on the articulatory data, the model is able to predict 71.5% of the articulatory state sequences using the acoustic input. Using optimized parameters, the proposed method shows a slight improvement for two speakers over the baseline phoneme recognition system which does not use articulatory knowledge. However, the improvement is only statistically significant for one of the speakers. While there is an improvement in recognition accuracy for the vowels, diphthongs and to some extent the semi-vowels, there is a decrease in accuracy for the remaining phonemes. Index Terms: phoneme recognition, articulatory measurements, Coupled-HMM." @default.
- W1622032071 created "2016-06-24" @default.
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- W1622032071 date "2009-09-06" @default.
- W1622032071 modified "2023-09-28" @default.
- W1622032071 title "On acquiring speech production knowledge from articulatory measurements for phoneme recognition" @default.
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- W1622032071 doi "https://doi.org/10.21437/interspeech.2009-428" @default.
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