Abstract
Speech recognition allows the machine to turn the speech signal into text through identification and understanding process. Extract the features, predict the maximum likelihood, and generate the models of the input speech signal are considered the most important steps to configure the Automatic Speech Recognition System (ASR). In this paper, an automatic Arabic speech recognition system was established using MATLAB and 24 Arabic words Consonant-Vowel Consonant-Vowel Consonant-Vowel (CVCVCV) was recorded from 19 Arabic native speakers, each speaker uttering the same word 3 times (total 1368 words). In order to test the system, 39-features were extracted by partitioning the speech signal into frames ~ 0.25 sec shifted by 0.10 sec. in back-end, the statistical models were generated by separated the features into number of states between 4 to 10, each state has 8-gaussian distributions. The data has 48 k sample rate and 32-bit depth and saved separately in a wave file format. The system was trained in phonetically rich and balanced Arabic speech words list (10 speakers * 3 times * 24 words, total 720 words) and tested using another word list (24 words * 9 speakers * 3 times *, total 648 words). Using different speakers similar words, the system obtained a very good word recognition accuracy results of 92.92% and a Word Error Rate (WER) of 7.08%.
Highlights
In order to test the system, 39-features were extracted by partitioning the speech signal into frames ~ 0.25 sec shifted by 0.10 sec. in back-end, the statistical models were generated by separated the features into number of states between 4 to 10, each state has 8-gaussian distributions
The most popular speech methods, Mel Cepstral frequency coefficients (MFCC) and Hidden Markov Model have been selected and tested in order to provide a high level of reliability and acceptability of the Arabic Automatic Speech Recognition System (ASR)
The feature vectors have been extracted for each sound using MFCC algorithm and saved, and the statistical models were generated using Hidden Markov Model classifier to match the data
Summary
The input signal is converted into a sequence of training and testing feature vectors saved in unique files. The most popular speech methods, Mel Cepstral frequency coefficients (MFCC) and Hidden Markov Model have been selected and tested in order to provide a high level of reliability and acceptability of the Arabic ASR. All the previous HMM parameters are used to generate the likelihood scores, which are used to find the best path between the frames in order to recognize the unknown word [10] [11]
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