Multi-resolution Spectral Entropy Feature for Robust ASR
Hemant Misra, Shajith Ikbal, Sunil Sivadas, H. Bourlard · 2006
Recently, entropy measures at different stages of recognition have been used in automatic speech recognition (ASR) tasks. In a recent paper, we proposed that formant positions of a spectrum can be captured by a multi-resolution spectral entropy feature. In this paper, we suggest modifications to the spectral entropy feature extraction approach and compute the entropy contribution from each sub-band to the total entropy of the normalized spectrum. Further, we explore the ideas of overlapping sub-bands and the time derivatives of the spectral entropy feature. The modified feature is robust to additive wide-band noise and performs well at low SNRs. Finally, in the TANDEM framework, we show that the system using combined entropy and PLP (perceptual linear prediction) features works better than the baseline PLP feature for additive wide-band noise at different SNRs.