Spectral Entropy Based Feature for Robust ASR
| Type of publication: | Conference paper |
| Citation: | misr04 |
| Booktitle: | Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
| Year: | 2004 |
| Month: | 5 |
| Address: | Montreal, Canada |
| Note: | IDIAP-RR 2003 56 |
| Crossref: | misra-rr-03-56: |
| Abstract: | 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. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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