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Automatic Phonetic Segmentation and Pronunciation Detection with Various Approaches of Acoustic Modeling

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Speech and Computer (SPECOM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11096))

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Abstract

The paper describes HMM-based phonetic segmentation realized by KALDI toolkit with the focus on study of accuracy of various acoustic modeling such as GMM-HMM vs. DNN-HMM, monophone vs. triphone, speaker independent vs. speaker dependent. The analysis was performed using TIMIT database and it proved the contribution of advanced acoustic modeling for the choice of a proper pronunciation variant. For this purpose, the lexicon covering the pronunciation variability among TIMIT speakers was created on the basis of phonetic transcriptions available in TIMIT corpus. When the proper sequence of phones is recognized by DNN-HMM system, more precise boundary placement can be then obtained using basic monophone acoustic models.

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Notes

  1. 1.

    Discussed in KALDI community at https://groups.google.com/forum/#!topic/kaldi-help/cSAm5iXGhZo.

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Acknowledgments

The research described in this paper was supported by internal CTU grant SGS17/183/OHK3/3T/13 “Special Applications of Signal Processing”.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Czech Technical University in Prague, K13131, Technicka 2, 166 27, Praha 6, Czech Republic

    Petr Mizera & Petr Pollak

Authors
  1. Petr Mizera
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  2. Petr Pollak
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Correspondence to Petr Pollak .

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Editors and Affiliations

  1. SPIIRAS, St. Petersburg, Russia

    Alexey Karpov

  2. Leipzig University of Telecommunications, Leipzig, Germany

    Oliver Jokisch

  3. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Mizera, P., Pollak, P. (2018). Automatic Phonetic Segmentation and Pronunciation Detection with Various Approaches of Acoustic Modeling. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-99579-3_44

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