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Detection of Replay Attacks using Single Frequency Filtering Cepstral CoefficientsAuthors: Raju Alluri K.N.R.K,Sivanand a,Sudarsana Reddy kadiri,Suryakanth V Gangashetty,Anil Kumar Vuppala Conference: Interspeech 2017 (Interspeech 2017 2017) Location Stockholm, Sweden Date: 2017-08-20 Report no: IIIT/TR/2017/23 AbstractAutomatic speaker verification systems are more vulnerable to spoofing attacks. Recently, various countermeasures have been developed for detecting high technology attacks such as speech synthesis and voice conversion. However, there is a wide gap in dealing with replay attacks. In this paper, we propose a new feature for replay attack detection based on single frequency filtering (SFF), which provides high temporal and spectral resolution at each instant. Single frequency filtering cepstral coefficients (SFFCC) with Gaussian mixture model classifier is used for the experimentation on the standard BTAS-2016 corpus. The previously reported best result, which is based on constant Q cepstral coefficients (CQCC) has achieved a half total error rate of 0.67 % on this data-set. Our proposed method outperforms the state of the art (CQCC) with a half total error rate of 0.0002 %. Full paper: pdf Centre for Language Technologies Research Centre |
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