Channel Robust Feature Transformation Based on Filter-Bank Energy Filtering

This correspondence proposes a novel feature transform for channel robustness with short utterances. In contrast to well-known techniques based on feature trajectory filtering, the presented procedure aims to reduce the time-varying component of channel distortion by applying a bandpass filter along the Mel frequency domain on a frame-by-frame basis. By doing so, the channel cancelling effect due to conventional feature trajectory filtering methods is enhanced. The filtering parameters are defined by employing a novel version of relative importance analysis based on a discriminant function. Experiments with telephone speech on a text-dependent speaker verification task show that the proposed scheme can lead to reductions of 8.6% in equal error rate when compared with the baseline system. Also, when applied in combination with cepstral mean normalization and RASTA, the presented technique leads to further reductions of 9.7% and 4.3% in equal error rate, respectively, when compared with those methods isolated.