http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

Speech processing algorithms are often developed demonstrating improvements over the state-of-the-art, but sometimes at the cost of high complexity. This makes algorithm reimplementations based on literature difficult, and thus reliable comparisons between published results and current work are hard to achieve. This paper presents a new collaborative and freely available repository for speech processing algorithms called COVAREP, which aims at fast and easy access to new speech processing algorithms and thus facilitating research in the field. We envisage that COVAREP will allow more reproducible research by strengthening complex implementations through shared contributions and openly available code which can be discussed, commented on and corrected by the community. Presently COVAREP contains contributions from five distinct laboratories and we encourage contributions from across the speech processing research field. In this paper, we provide an overview of the current offerings of COVAREP and also include a demonstration of the algorithms through an emotion classification experiment.

/pdf/covarep-a-collaborative-voice-analysis-repository-for-speech-4ovkoxkaly.pdf

COVAREP — A collaborative voice analysis repository for speech technologies

http://cs.uef.fi/pages/tkinnu/webpage/pdf/spoofingsurvey2014_SPCOM.pdf

Spoofing and countermeasures for speaker verification

This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.

/pdf/speech-synthesis-based-on-hidden-markov-models-45y6v1rk5u.pdf

Speech Synthesis Based on Hidden Markov Models

The detection of relative motion or orientation between a user and a computing device can be used to control aspects of the device. For example, the computing device can include an imaging element and software for locating positions, shapes, separations, and/or other aspects of a user's facial features relative to the device, such that an orientation of the device relative to the user can be determined. A user then can provide input to the device by performing actions such as tilting the device, moving the user's head, making a facial expression, or otherwise altering an orientation of at least one aspect of the user with respect to the device. Such an approach can be used in addition to, or as an alternative to, conventional input devices such as keypads and touch screens.

Movement recognition as input mechanism

In this paper, we evaluate the vulnerability of speaker verification (SV) systems to synthetic speech. The SV systems are based on either the Gaussian mixture model–universal background model (GMM-UBM) or support vector machine (SVM) using GMM supervectors. We use a hidden Markov model (HMM)-based text-to-speech (TTS) synthesizer, which can synthesize speech for a target speaker using small amounts of training data through model adaptation of an average voice or background model. Although the SV systems have a very low equal error rate (EER), when tested with synthetic speech generated from speaker models derived from the Wall Street Journal (WSJ) speech corpus, over 81% of the matched claims are accepted. This result suggests vulnerability in SV systems and thus a need to accurately detect synthetic speech. We propose a new feature based on relative phase shift (RPS), demonstrate reliable detection of synthetic speech, and show how this classifier can be used to improve security of SV systems.

/pdf/evaluation-of-speaker-verification-security-and-detection-of-44lec7inz4.pdf

Evaluation of Speaker Verification Security and Detection of HMM-Based Synthetic Speech

In this paper, we present new results from our research into the vulnerability of a speaker verification (SV) system to synthetic speech. We use a HMM-based speech synthesizer, which creates synthetic speech for a targeted speaker through adaptation of a background model and both GMM-UBM and support vector machine (SVM) SV systems. Using 283 speakers from the Wall-Street Journal (WSJ) corpus, our SV systems have a 0.35% EER. When the systems are tested with synthetic speech generated from speaker models derived from the WSJ journal corpus, over 91% of the matched claims are accepted. We propose the use of relative phase shift (RPS) in order to detect synthetic speech and develop a GMM-based synthetic speech classifier (SSC). Using the SSC, we are able to correctly classify human speech in 95% of tests and synthetic speech in 88% of tests thus significantly reducing the vulnerability.

/pdf/detection-of-synthetic-speech-for-the-problem-of-imposture-3682g2wymr.pdf

Detection of synthetic speech for the problem of imposture

In this paper, we evaluate the vulnerability of a speaker verification (SV) system to synthetic speech. Although this problem was first examined over a decade ago, dramatic improvements in both SV and speech synthesis have renewed interest in this problem. We use a HMM-based speech synthesizer, which creates synthetic speech for a targeted speaker through adaptation of a background model and a GMM-UBM-based SV system. Using 283 speakers from the Wall-Street Journal (WSJ) corpus, our SV system has a 0.4% EER. When the system is tested with synthetic speech generated from speaker models derived from the WSJ journal corpus, 90% of the matched claims are accepted. This result suggests a possible vulnerability in SV systems to synthetic speech. In order to detect synthetic speech prior to recognition, we investigate the use of an automatic speech recognizer (ASR), dynamic-timewarping (DTW) distance of mel-frequency cepstral coefficients (MFCC), and previously-proposed average inter-frame difference of log-likelihood (IFDLL). Overall, while SV systems have impressive accuracy, even with the proposed detector, high-quality synthetic speech can lead to an unacceptably high acceptance rate of synthetic speakers.

/pdf/evaluation-of-the-vulnerability-of-speaker-verification-to-3y3nh2amle.pdf

Evaluation of the Vulnerability of Speaker Verification to Synthetic Speech

https://hal.archives-ouvertes.fr/hal-00592584/document

Modeling and interpolation of Austrian German and Viennese dialect in HMM-based speech synthesis

In this paper, we investigate imposture using synthetic speech. Although this problem was first examined over a decade ago, dramatic improvements in both speaker verification (SV) and speech synthesis have renewed interest in this problem. We use a HMM-based speech synthesizer which creates synthetic speech for a targeted speaker through adaptation of a background model. We use two SV systems: standard GMM-UBM-based and a newer SVM-based. Our results show when the systems are tested with human speech, there are zero false acceptances and zero false rejections. However, when the systems are tested with synthesized speech, all claims for the targeted speaker are accepted while all other claims are rejected. We propose a two-step process for detection of synthesized speech in order to prevent this imposture. Overall, while SV systems have impressive accuracy, even with the proposed detector, high-quality synthetic speech will lead to an unacceptably high false acceptance rate.

/pdf/revisiting-the-security-of-speaker-verification-systems-47s820cv1j.pdf

Michael Pucher

Papers

Evaluation of Speaker Verification Security and Detection of HMM-Based Synthetic Speech

Detection of synthetic speech for the problem of imposture

Evaluation of the Vulnerability of Speaker Verification to Synthetic Speech

Modeling and interpolation of Austrian German and Viennese dialect in HMM-based speech synthesis

Revisiting the security of speaker verification systems against imposture using synthetic speech