This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the speech segments accurately. Experimental results show that this algorithm outperforms the energy-based algorithms in both detection accuracy and recognition performance under noisy environments, with an average error rate reduction of more than 16%.
Cite as: Shen, J.-L., Hung, J.-W., Lee, L.-S. (1998) Robust entropy-based endpoint detection for speech recognition in noisy environments. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0232, doi: 10.21437/ICSLP.1998-527
@inproceedings{shen98b_icslp,
author={Jia-Lin Shen and Jeih-Weih Hung and Lin-Shan Lee},
title={{Robust entropy-based endpoint detection for speech recognition in noisy environments}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0232},
doi={10.21437/ICSLP.1998-527}
}