Morphological filtering of speech spectrograms in the context of additive noise

A recent approach to signal segmentation in additive noise [1, 2] uses features of small spectrogram sub-units accrued over the full spectrogram. The original work considered chirp signals in additive white Gaussian noise. This paper extends this work first by considering similar signals at different signal-to-noise ratios and then in the context of speech recognition. For the chirp case, a cost function based on spectrogram area is introduced and this indicates that the segmentation process is robust down to and below 0 dB SNR. For the speech experiments the objectives are again to assess the segmentation capabilities of the process. White Gaussian noise is added to clean speech and the segmentation process applied. The cost function now is automatic speech recognition (ASR) accuracy. After segmentation speech areas are set to one constant level and non-speech areas are set to a lower constant level, thereby assessing the segmentation process and the importance of spectral shape in ASR. For the ASR experiments the TIDigits database is used in a standard AURORA 2 configuration, under mis-matched test and training conditions. With 5 dB SNR for the test set only (clean training) a word accuracy of 56% is achieved. This compares with 16% when the same noisy test data is applied directly to the ASR system without segmentation. Thus the segmentation approach shows that spectral shapes alone (without normal spectral amplitude variations) leads to perhaps surprisingly good ASR results in noisy conditions. The next stage is to include amplitude information along with appropriate noise compensation.