Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences record...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Zarqa Private University, Jordan
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/6980/ http://irep.iium.edu.my/6980/ http://irep.iium.edu.my/6980/1/Arabic_Speaker-Independent_Continuous__Arab_Journal.pdf http://irep.iium.edu.my/6980/4/Arabic_Speaker-Independent_Continuous_.pdf |
| Summary: | This paper describes and proposes an efficient and effective framework for the design and development of a
speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced
speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native
speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world.
The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the
Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models
(HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the
acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state
distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but
different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of
11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system
obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical
marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of
89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively. |
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