Sciweavers

Share
ICASSP
2011
IEEE

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

8 years 3 months ago
Amplitude modulation spectrogram based features for robust speech recognition in noisy and reverberant environments
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.
Niko Moritz, Jörn Anemüller, Birger Koll
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Niko Moritz, Jörn Anemüller, Birger Kollmeier
Comments (0)
books