Projects per year
Abstract
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.
Original language | English |
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Pages (from-to) | 2280-2290 |
Number of pages | 11 |
Journal | IEEE Transactions on Audio, Speech and Language Processing |
Volume | 20 |
Issue number | 8 |
DOIs | |
Publication status | Published - Oct 2012 |
Keywords / Materials (for Non-textual outputs)
- Adaptation models
- Hidden Markov models
- Speech
- Support vector machines
- Synthesizers
- Training
- Vectors
- Security
- speaker recognition
- speech synthesis
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- 2 Finished
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Deep architectures for statistical speech synthesis
Yamagishi, J.
UK industry, commerce and public corporations
4/09/12 → 3/03/16
Project: Research
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