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Amplitude modulation spectrogram based features for robust speech recognition in noisy and reverberant environments

: Moritz, N.; Anemüller, J.; Kollmeier, B.


IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011. Vol.7 : Prague, Czech Republic, 22 - 27 May 2011
Piscataway/NJ: IEEE, 2011
ISBN: 978-1-4577-0538-0 (Print)
ISBN: 978-1-4577-0539-7
ISBN: 978-1-4577-0537-3 (Online)
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <36, 2011, Prague>
Conference Paper
Fraunhofer IDMT ()

In this contribution we present a feature extraction method that relies on the modulation-spectral analysis of amplitude fluctuations within sub-bands of the acoustic spectrum by a STFT. The experimental results indicate that the optimal temporal filter extension for amplitude modulation analysis is around 310 ms. It is also demonstrated that the phase information of the modulation spectrum contains important cues for speech recognition. In this context, the advantage of an odd analysis basis function is considered. The best presented features reached a total relative improvement of 53,5 % for clean-condition training on Aurora-2. Furthermore, it is shown that modulation features are more robust against room reverberation than conventional cepstral and dynamic features and that they strongly benefit from a high early-to-late energy ratio of the characteristic RIR.