Articulo
Channel robust feature transformation based on filter-bank energy filtering
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING;
IEEE Trans. Audio Speech Lang. Process.
Registro en:
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D05I10243
D05I10243
WOS:000278814600020
1558-7916
Autor
BECERRA-YOMA, NESTOR
GARRETON-VENDER, CLAUDIO
TORRES, MATIAS
Institución
Resumen
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. This work was supported by Conicyt-Chile under Grants Fondef D05I-10243 and Fondecyt 1070382/1100195. 3 FONDEF nbecerra@ing.uchile.cl Conicyt-Chile [D05I-10243]; Fondecyt [1070382/1100195] 5 FONDEF 18