Hidden Markov Model for Speech Recognition Using Modified Forward-Backward Re-estimation Algorithm
There are various kinds of practical implementation issues for the HMM. The use of scaling factor is the main issue in HMM implementation. The scaling factor is used for obtaining smoothened probabilities. The proposed technique called Modified Forward-Backward Re-estimation algorithm used to recognize speech patterns. The proposed algorithm has shown very good recognition accuracy as compared to the conventional Forward-Backward Re-estimation algorithm.
Keywords: Forward-Backward Algorithm; Speech Recognition, Parameter Estimation, Viterbi Algorithm, Baum-Welch Algorithm.
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ABOUT THE AUTHOR
Balwant A. Sonkamble
He received his BE (Computer science and Engineering in 1994 and M. E. (Computer Engineering) in 2004. Currently he is research scholar at SGGS College of Engineering and Technology, Vishnupuri, Nanded (MS)–INDIA. He is working as a Associate Professor in Computer Engineering at Pune Institute of Computer Technology, Pune, India. His research areas are Speech Recognition and Artificial Intelligence.
Balwant A. Sonkamble
He received his BE (Computer science and Engineering in 1994 and M. E. (Computer Engineering) in 2004. Currently he is research scholar at SGGS College of Engineering and Technology, Vishnupuri, Nanded (MS)–INDIA. He is working as a Associate Professor in Computer Engineering at Pune Institute of Computer Technology, Pune, India. His research areas are Speech Recognition and Artificial Intelligence.