Speech Recognition System Using by Abushariah Ahmad (12 results)

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Research in automatic speech recognition has been done for almost four decades. This project aims to develop automated English digits speech recognition system using Matlab. The system is able to recognize the spoken utterances by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique, which then estimates the observation likelihood by using the Forward algorithm. The Hidden Markov Model (HMM) parameters are estimated by applying the Baum-Welch algorithm on previously trained samples. The most likely sequence is then decoded using Viterbi algorithm, thus producing the recognized word. This project 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 relatively sucessful recognition rates. The samples of Matlab codes were provided in the Appendix. 112 pp. Englisch.…

Condition: New. Print on Demand pp. 112 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abushariah Ahmad A. M.::: Ahmad Abushariah graduated from International Islamic University Malaysia in 2009, and is currently pursuing Master degree at IIUM. His research interest is speech and image processing. ::: Teddy Surya Gunaw.…

Condition: New. PRINT ON DEMAND pp. 112.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Research in automatic speech recognition has been done for almost four decades. This project aims to develop automated English digits speech recognition system using Matlab. The system is able to recognize the spoken utterances by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique, which then estimates the observation likelihood by using the Forward algorithm. The Hidden Markov Model (HMM) parameters are estimated by applying the Baum-Welch algorithm on previously trained samples. The most likely sequence is then decoded using Viterbi algorithm, thus producing the recognized word. This project 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 relatively sucessful recognition rates. The samples of Matlab codes were provided in the Appendix.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.…