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Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition

International Journal of Speech Technology volume 19, pages 495–508 (2016)Cite this article

Abstract

In attempt to increase the rate of Arabic phonemes recognition, we introduce a novel hybrid recognition algorithm. The algorithm is composed of the learning vector quantization (LVQ) and hidden Markov model (HMM). The hybrid algorithm used to recognizing Arabic phonemes in continuous open-vocabulary speech. A recorded Arabic corpus of different TV news for modern standard Arabic was used for training and testing purposes. We employ a data driven approach to generate the training feature vectors that embed the frame neighboring correlation information. Next, we generate the phonemes codebooks using the K-means splitting algorithm. Then, we trained the generated codebooks using the LVQ algorithm. We achieved a performance of 98.49 % during independent classification training and 90 % during dependent classification training. When using the trained LVQ codebooks in Arabic utterance transcription, the phoneme recognition rate was 72 % using LVQ only. We combined the LVQ codebooks with the single state HMM model using enhanced Viterbi algorithm which includes the phonemes bigrams. We achieved 89 % of Arabic phonemes recognition rate based on the hybrid LVQ/HMM algorithm.

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Notes

  1. Except for the first 3 frames and the last 3 frames in the feature matrix.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Computer Sciences and Information Technology, Yarmouk University, Irbid, 21163, Jordan

    Khalid M. O. Nahar

  2. Computer Science Department, Faculty of Computer Science and Information Technology, Jarash University, Jarash, Jordan

    Mohammed Abu Shquier

  3. Information & Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Kingdom of Saudi Arabia

    Wasfi G. Al-Khatib & Husni Al-Muhtaseb

  4. Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Kingdom of Saudi Arabia

    Moustafa Elshafei

Authors
  1. Khalid M. O. Nahar
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  2. Mohammed Abu Shquier
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  3. Wasfi G. Al-Khatib
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  4. Husni Al-Muhtaseb
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  5. Moustafa Elshafei
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Correspondence to Khalid M. O. Nahar.

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Nahar, K.M.O., Abu Shquier, M., Al-Khatib, W.G. et al. Arabic phonemes recognition using hybrid LVQ/HMM model for continuous speech recognition. Int J Speech Technol 19, 495–508 (2016). https://doi.org/10.1007/s10772-016-9337-5

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  • DOI: https://doi.org/10.1007/s10772-016-9337-5

Keywords

  • Learning vector quantization (LVQ)
  • Codebooks
  • K-means algorithm
  • Phonemes transcription
  • Hidden Markov model (HMM)
  • Hybrid LVQ/HMM model