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

Khalid M.O. Nahar*, Mohammed Abu Shquier, Wasfi G. Al-Khatib, Husni Al-Muhtaseb, Moustafa Elshafei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

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.

Original languageEnglish
Pages (from-to)495-508
Number of pages14
JournalInternational Journal of Speech Technology
Volume19
Issue number3
DOIs
StatePublished - 1 Sep 2016

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media New York.

Keywords

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

ASJC Scopus subject areas

  • Software
  • Language and Linguistics
  • Human-Computer Interaction
  • Linguistics and Language
  • Computer Vision and Pattern Recognition

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