Time and frequency filtering of filter-bank energies for robust HMM speech recognition
Speech Communication - Special issue on noise robust ASR
THE SRI NIST 2008 speaker recognition evaluation system
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A comparative study of feature and score normalization for speaker verification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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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.