Spectral Entropy Based Feature for Robust ASR

In general, entropy gives us a measure of the number of bits required to represent some information. When applied to probability mass function (PMF), entropy can also be used to measure the ``peakiness'' of a distribution. In this paper, we propose using the entropy of short time Fourier transform spectrum, normalised as PMF, as an additional feature for automatic speech recognition (ASR). It is indeed expected that a peaky spectrum, representation of clear formant structure in the case of voiced sounds, will have low entropy, while a flatter spectrum corresponding to non-speech or noisy regions will have higher entropy. Extending this reasoning further, we introduce the idea of multi-band/multi-resolution entropy feature where we divide the spectrum into equal size sub-bands and compute entropy in each sub-band. The results presented in this paper show that multi-band entropy features used in conjunction with normal cepstral features improve the performance of ASR system.


Published in:
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Presented at:
Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Year:
2004
Publisher:
Montreal, Canada
Keywords:
Note:
IDIAP-RR 2003 56
Laboratories:




 Record created 2006-03-10, last modified 2018-01-27

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