Speech Magnitude-Spectrum Information-Entropy (MSIE) for Automatic Speech Recognition in Noisy Environments

The Magnitude-Spectrum Information-Entropy (MSIE) of the speech signal is presented as an alternative representation of the speech that can be used to mitigate the mismatch between training and testing conditions. The speech-magnitude spectrum is considered as a random variable from which entropy coefficients can be calculated for each frame. By concatenating these entropic coefficients to its corresponding MFCC vector, then calculating the dynamic coefficients, Δ and ΔΔ, the results show an improvement compared to a baseline. The MSIE effectiveness was tested under the Aurora 2 database audio files. When trained in clean speech, the experimental results obtained by the MSIE concatenated to the MFCC outperform the results obtained with the MFCC baseline system for selected types of noises at different SNRs. For this selected group of noises the overall improvement performance in the range 0 dB to 20 dB for the Aurora 2 database is of 15.06%.