Novel speech features for improved detection of spoofing attacks

作者: Dipjyoti Paul , Monisankha Pal , Goutam Saha

DOI: 10.1109/INDICON.2015.7443805

关键词: Spoofing attackPattern recognitionVoice activity detectionBiometricsArtificial intelligenceSpeech synthesisHidden Markov modelSpeech recognitionFeature extractionBlock (data storage)Mel-frequency cepstrumComputer science

摘要: Now-a-days, speech-based biometric systems such as automatic speaker verification (ASV) are highly prone to spoofing attacks by an imposture. With recent development in various voice conversion (VC) and speech synthesis (SS) algorithms, these can pose a serious potential threat the current state-of-the-art ASV systems. To impede enhance security of systems, efficient anti-spoofing algorithms is essential that differentiate synthetic or converted from natural human speech. In this paper, we propose set novel features for detecting attacks. The proposed computed using alternative frequency-warping technique formant-specific block transformation filter bank log energies. We have evaluated existing against several kinds data ASVspoof 2015 corpora. results show techniques outperform approaches attack detection task. investigated paper also accurately classify equal error rates (EERs) 0% been achieved.

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