English digits speech recognition system based on Hidden Markov Models
This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech wavefo...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/2328/4/English_Digits_Speech_Recognition_System_Based_on_Hidden_Markov_Models.pdf |
| _version_ | 1796874880732364800 |
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| author | Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud |
| author_facet | Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud |
| author_sort | Abushariah, Ahmad A. M. |
| collection | IIUM |
| description | This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique This paper focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed a successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 72.5% whereas the speaker-independent mode achieved 56.25%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 82.5% whereas the speaker-independent mode achieved 76.67%. These recognition rates are relatively successful if compared to similar systems. |
| first_indexed | 2024-03-05T22:30:49Z |
| format | Proceeding Paper |
| id | oai:generic.eprints.org:2328 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:30:49Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:23282011-11-24T06:00:48Z http://irep.iium.edu.my/2328/ English digits speech recognition system based on Hidden Markov Models Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique This paper focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed a successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 72.5% whereas the speaker-independent mode achieved 56.25%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 82.5% whereas the speaker-independent mode achieved 76.67%. These recognition rates are relatively successful if compared to similar systems. 2010 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/2328/4/English_Digits_Speech_Recognition_System_Based_on_Hidden_Markov_Models.pdf Abushariah, Ahmad A. M. and Gunawan, Teddy Surya and Khalifa, Othman Omran and Abushariah, Mohammad Abd-Alrahman Mahmoud (2010) English digits speech recognition system based on Hidden Markov Models. In: International Conference on Computer and Communication Engineering ICCCE 2010, 11-13 May, 2010, Kuala Lumpur, Malaysia. http://www.iium.edu.my/iccce/10/ |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Abushariah, Ahmad A. M. Gunawan, Teddy Surya Khalifa, Othman Omran Abushariah, Mohammad Abd-Alrahman Mahmoud English digits speech recognition system based on Hidden Markov Models |
| title | English digits speech recognition system based on Hidden Markov Models |
| title_full | English digits speech recognition system based on Hidden Markov Models |
| title_fullStr | English digits speech recognition system based on Hidden Markov Models |
| title_full_unstemmed | English digits speech recognition system based on Hidden Markov Models |
| title_short | English digits speech recognition system based on Hidden Markov Models |
| title_sort | english digits speech recognition system based on hidden markov models |
| topic | TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| url | http://irep.iium.edu.my/2328/4/English_Digits_Speech_Recognition_System_Based_on_Hidden_Markov_Models.pdf |
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