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SFF Anti-Spoofer: IIIT-H Submission for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017Authors: 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/2017/22 AbstractThe ASVspoof 2017 challenge is about the detection of replayed speech from human speech. The proposed system makes use of the fact that when the speech signals are replayed, they pass through multiple channels as opposed to original recordings. This channel information is typically embedded in low signal to noise ratio regions. A speech signal processing method with high spectro - temporal resolution is required to extract robust features from such regions. The single frequency filtering (SFF) is one such technique, which we propose to use for replay attack detection. While SFF based feature representation was used at front-end, Gaussian mixture model and bi-directional long short-term memory models are investigated at the backend as classifiers. The experimental results on ASVspoof 2017 dataset reveal that, SFF based representation is very effective in detecting replay attacks. The score level fusion of back end classifiers further improved the performance of the system which indicates that both classifiers capture complimentary information. Full paper: pdf Centre for Language Technologies Research Centre |
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