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 [BibTeX] [Marc21]
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},
Projects Idiap
Authors Misra, Hemant
Ikbal, Shajith
Bourlard, Hervé
Hermansky, Hynek
Added by: [UNK]
Total mark: 0
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