Robust feature extraction based on an asymmetric level-dependent auditory filterbank and a subband spectrum enhancement technique
We propose cGCFB-based robust cepstral feature (RCGCC) for speech recognition tasks. A sigmoid-shaped suppression rule is introduced for auditory spectrum enhancement. Short-term cepstral mean and scale normalization is proposed to reduce mismatch. Performance evaluation is carried out on the AURORA-2, -4 and -5 corpora. Proposed RCGCC outperformed other front-ends in real-time reverberant environment.
speech recognition auditory spectrum enhancement compressive gammachirp feature normalization