Voice conversion spoofing detection by exploring artifacts estimates

Abstract

Automatic speaker verification or voice biometrics is an approach to verify the person’s claimed identity through his/her voice. Voice biometrics finds its application in mobile banking and forensics. With the increased usage of speaker verification systems, studying the spoofing threats to speaker verification systems and building proper countermeasure is gaining attention. Spoofing is a genuine challenge as it leads to increase in the false alarm rate, i.e. an impostor is incorrectly accepted as genuine speaker. To make voice biometrics viable for practical applications there is a need to detect spoofing attack. Voice conversion spoofing is a technique where the imposter speaker’s speech is converted to desired speaker’s speech using signal processing approaches. Studies show that voice conversion introduces artifacts in resultant speech, hence, this paper proposes a novel approach to detect voice conversion spoofing attack by estimating artifact estimates from the given speech signal. To obtain artifact estimate from speech signal non-negative matrix factorization based source separation technique is employed. Later, Convolutional Neural Network based binary classifier is built to classify artifact estimates of input speech as natural and synthetic speech. Experiments are conducted on voice conversion challenge 2016 and voice conversion challenge 2018 database. Results show that proposed technique gives excellent performance by detecting wide range of unknown attacks. The proposed systems are compared to state of art spoof detection systems based on Constant Q Cepstrum Coefficients and Linear Frequency Cepstral Coefficients and results show the proposed system give relatively equivalent and/or better performance. Validation results for various noises is studied using NOIZEUS database and results show the efficiency of the proposed system in noisy environments.