Utilizing intelligent segmentation in isolated word recognition using a hybrid HTD-HMM

Authors:
R. Kazemi;A. Rezazadeh Sereshkeh;B. Ehsandoust
Affiliations:
Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran;Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran;Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Venue:
CISST '11 Proceedings of the 5th WSEAS international conference on Circuits, systems, signal and telecommunications
Year:
2011

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Abstract

Isolated Word Recognition (IWR) is becoming increasingly attractive due to the improvement of speech recognition techniques. However, the accuracy of IWR suffers when large databases or words with similar pronunciation are used. The criterion for accurate speech recognition is suitable segmentation. However, the traditional method of segmentation equal segmentation does not produce the most accurate result. Furthermore, utilizing manual segmentation based on events is not possible in large databases. In this paper, we introduce an intelligent segmentation based on Hierarchical Temporal Decomposition (HTD). Based on this method, a temporal decomposition (TD) algorithm can be used to categorize words into groups with the same number of segments. The HTD method can then be utilized to segment all the words in each group to the number of events of the biggest group in the previous step. These segments will be processed with the hidden Markov model (HMM). Experimental results show that the proposed method significantly improves the recognition accuracy when compared to traditional segmentation.