CONF misr04/IDIAP Spectral Entropy Based Feature for Robust ASR Misra, Hemant Ikbal, Shajith Bourlard, Hervé Hermansky, Hynek EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-56.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/misra-rr-03-56 Related documents Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2004 Montreal, Canada May 2004 IDIAP-RR 2003 56 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. REPORT misra-rr-03-56/IDIAP Spectral Entropy Based Feature for Robust ASR Misra, Hemant Ikbal, Shajith Bourlard, Hervé Hermansky, Hynek EXTERNAL https://publications.idiap.ch/attachments/reports/2003/rr03-56.pdf PUBLIC Idiap-RR-56-2003 2003 IDIAP Martigny, Switzerland in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing {(ICASSP)}, 2004 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.