Al-Diri, Bashir and Sharieh, Ahmad and Qutiashat, Munib (2007) A speech recognition model based on tri-phones for the Arabic language. Advances in modelling Series B: Signal processing and pattern recognition, 50 (2). pp. 49-64. ISSN 1240-4543
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One way to keep up a decent recognition of results- with increasing vocabulary- is the use of base units rather than words. This paper presents a Continuous Speech Large Vocabulary Recognition System-for Arabic, which is based on tri-phones. In order to train and test the system, a dictionary and a 39-dimensional Mel Frequency Cepstrum Coefficient (MFCC) feature vector was computed. The computations involve: Hamming Window, Fourier Transformation, Average Spectral Value (ASV), Logarithm of ASV, Normalized Energy, as well as, the first and second order time derivatives of 13-coefficients. A combination of a Hidden Markov Model and a Neural Network Approach was used in order to model the basic temporal nature of the speech signal. The results obtained by testing the recognizer system with 7841 tri-phones. 13-coefficients indicate accuracy level of 58%. 39-coeefficents indicates 62%. With Cepstrum Mean Normalization, there is an indication of 71%. With these small available data-only 620 sentences-these results are very encouraging.
|Keywords:||A Speech Recognition, Automatic Speech Recognition, Tri-phones, Mel Frequency Cepstrum Coefficient, Hidden Markov Model, Neural Network|
|Subjects:||G Mathematical and Computer Sciences > G700 Artificial Intelligence|
G Mathematical and Computer Sciences > G710 Speech and Natural Language Processing
|Divisions:||College of Science > School of Computer Science|
|Deposited By:||Bashir Al-Diri|
|Deposited On:||02 Dec 2009 11:14|
|Last Modified:||04 Jun 2014 15:09|
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