| Paper: | SP-L9.1 | ||
| Session: | Robust Features for Speech Recognition | ||
| Time: | Friday, May 21, 13:00 - 13:20 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Robust Speech Recognition | ||
| Title: | SPECTRAL ENTROPY BASED FEATURE FOR ROBUST ASR | ||
| Authors: | Hemant Misra; IDIAP | ||
| Shajith Ikbal; IDIAP | |||
| Hervé Bourlard; IDIAP | |||
| Hynek Hermansky; IDIAP | |||
| 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 flatterspectrum 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. | ||
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