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ICASSP
2011
IEEE

Robust speaker identification using a CASA front-end

7 years 9 months ago
Robust speaker identification using a CASA front-end
Speaker recognition remains a challenging task under noisy conditions. Inspired by auditory perception, computational auditory scene analysis (CASA) typically segregates speech by producing a binary time-frequency mask. We first show that a recently introduced speaker feature, Gammatone Frequency Cepstral Coefficient, performs substantially better than conventional speaker features under noisy conditions. To deal with noisy speech, we apply CASA separation and then either reconstruct or marginalize corrupted components indicated by the CASA mask. Both methods are effective. We further combine them into a single system depending on the detected signal to noise ratio (SNR). This system achieves significant performance improvements over related systems under a wide range of SNR conditions.
Xiaojia Zhao, Yang Shao, DeLiang Wang
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Xiaojia Zhao, Yang Shao, DeLiang Wang
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