Feature subsets and hidden Markov model (HMM) parameters are the two major factors that affect the classification accuracy (CA) of the HMM-based classifier. This paper proposes a genetic algorithm based approach for simultaneously optimizing both feature subsets and HMM parameters with the aim to obtain the best HMM-based classifier. Experimental data extracted from three spontaneous speech corpora were used to evaluate the effectiveness of the proposed approach and the three other approaches (i.e. the approaches to single optimization of feature subsets, single optimization of HMM parameters, and no optimization of both feature subsets and HMM parameters) that were adopted in the previous work for discrimination between speech and non-speech events (e.g. filled pause, laughter, applause). The experimental results show that the proposed approach obtains CA of 91.05%, while the three other approaches obtain CA of 86.11%, 87.05%, and 83.16%, re-spectively. The results suggest that the proposed approach is superior to the previous approaches. © 2010 Springer Science+Business Media, LLC.
Bibliographical noteThis work was supported by City University of Hong Kong Strategic Grant 7002441, the National Natural Science Foundation of China (Item No. 60972132), and the Natural Science Foundation of Guangdong Province, China (Item No. 9351064101000003).
- Genetic algorithm
- Hidden Markov model
- Non-speech events
- Simultaneous optimization
- Spontaneous speech processing