SuperLectures.com

Evaluation of the Vulnerability of Speaker Verification to Synthetic Speech

SESSION 7: Speaker and Language recognition - Evaluations and performance testing

Přidáno: 14. 7. 2010 11:08, Autor: Phillip DeLeon (New Mexico State University), Michael Pucher (Telecommunications Research Center (FTW)), Junichi Yamagishi (University of Edinburgh), Délka: 0:33:36

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-time-warping (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.


  Přepis řeči

|

  Komentáře

Please sign in to post your comment!

  Informace o přednášce

Počet zhlédnutí: 813
Rozlišení videa: 720x576 px
Audio stopa: MP3 [11.53 MB], 0:33:36