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.
|Number of pages||14|
|Journal||International Journal of Speech Technology|
|State||Published - 1 Sep 2016|
Bibliographical notePublisher Copyright:
© 2016, Springer Science+Business Media New York.
- Hidden Markov model (HMM)
- Hybrid LVQ/HMM model
- K-means algorithm
- Learning vector quantization (LVQ)
- Phonemes transcription
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Linguistics and Language
- Computer Vision and Pattern Recognition