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Lhasa Dialect Recognition of Different Phonemes Based on TDNN Method

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13339))

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Abstract

Time-delay neural networks (TDNN) acoustic models have significantly advanced forward in recent years, outperforming the traditional Gaussian Mixture Hidden Markov Model (GMM-HMM) in large vocabulary continuous speech recognition tasks. We try to develop a practical Lhasa Tibetan ASR system. For higher speech recognition accuracy, in this paper, we consider to investigate the performances of Tibetan acoustic modeling using TDNN method based on several different phoneme sets, which are defined based on linguistic and phonological knowledge of Tibetan Lhasa dialect. Experiments are conducted on a Tibetan corpus recorded by 20 persons, using a bigram language model over phones. The phone error rate (PER) results show that the acoustic model with CTL set performs best, which is relatively 10.43% higher accuracy than the basic phoneme set. Moreover, our results confirm the fact that for Lhasa Tibetan acoustic model, the paradigm TDNN-HMM outperforms the conventional GMM-HMM.

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Acknowledgements

This work was supported by the Regional Innovation Cooperation Project of Sichuan Province(Grant No. 22QYCX0082).

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Authors and Affiliations

  1. Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission, Qinghai Minzu University, Xining, 810007, China

    Kuntharrgyal Khysru, Yangzhuoma Qie, Qilong Sun & Jianguo Wei

  2. Institute of “TWO-BOMBS&ONE-SATELLITE” Ideals and Beliefs, Beijing, 810299, China

    Haiqiang Shi

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300072, China

    Jianguo Wei

Authors
  1. Kuntharrgyal Khysru
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  2. Yangzhuoma Qie
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  3. Haiqiang Shi
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  4. Qilong Sun
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  5. Jianguo Wei
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Corresponding author

Correspondence to Kuntharrgyal Khysru .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Khysru, K., Qie, Y., Shi, H., Sun, Q., Wei, J. (2022). Lhasa Dialect Recognition of Different Phonemes Based on TDNN Method. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_13

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  • DOI: https://doi.org/10.1007/978-3-031-06788-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06787-7

  • Online ISBN: 978-3-031-06788-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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