2. A Tutorial on Speaker Verification

3. First A. Author, Second B. Author, and Third C. Author

4. Statistical Modeling Speaker Verification via Likelihood Ratio Detection

5. The single-speaker detection task can be stated as a basic hypothesis test between two hypotheses

7. Figure Likelihood ratio based speaker verification system.

10. where is some function, such as average or maximum, of the likelihood values from the background speaker set. The selection, size, and combination of the background speakers have been the subject of much research,,. In general, it has been found that to obtain the best performance with this approach requires the use of speaker-specific background speaker sets. This can be a drawback in an applications using a large number of hypothesized speakers, each requiring their own background speaker set.

11. T. Matsui and S. Furui, УSimilarity normalization methods for speaker verification based on a posteriori probability,Ф in Proceedings of the ESCA Workshop on Automatic Speaker Recognition, Identification and Verification, pp. 59-62, 1994. Gaussian Mixture Models

15. Adapted GMM System

16. The specifics of the adaptation are as follows. Given a background model and training vectors from the hypothesized speaker, we first determine the probabilistic alignment of the training vectors into the background model mixture components. That is, for mixture i in the background model, we compute