1. Decision Making Based on Cohort Scores for
Speaker Verification
Lantian Li
CSLT / RIIT, Tsinghua University
Co-work with Renyu Wang, Caixia Wang and Thomas Fang Zheng
IEEE APSIPA ASC, Dec , 2016
APSIPA presentation
1/13/2019
APSIPA ASC 2016

2. Outline Introduction Cohort-based decision making framework
A single-score decision making
Solution by a multi-score decision making
Cohort-based decision making framework
Cohort selection
Feature design
Discriminative model training
Experiments
Conclusions
1/13/2019
APSIPA ASC 2016

3. Introduction Speaker recognition Decision making
The single-score decision is simple and efficient
Quite sensitive to variations (score variation)
Text contents, channel, speaking styles.
Difficulty in choosing an appropriate threshold
Error-pron decision
In a typical GMM-UBM system, the score is often computed as the log likelihood ratio that the test utterance being generated from the GMM of the claimed speaker and the UBM.
Leads to the err-pron decsion
1/13/2019
APSIPA ASC 2016

4. Introduction Score normalization techniques
Bayes’ theorem (Z-norm, T-norm, etc.)
Cohort normalization
Cohort replaces the UBM
The alternative hypothesis more accurately
It is also simply averaged to normalize the target score.
 Still a single-score approach
1/13/2019
APSIPA ASC 2016

5. Motivations Our idea A new cohort approach
Cohort normalization is not just a mean average.
Cohort scores: distributions, ranks, spanning areas, etc.
A new cohort approach
Decision on the whole cohort sets
Employ a powerful discriminative model
A true and reliable multi-score decision making
1/13/2019
APSIPA ASC 2016

6. Cohort-based decision making framework
Cohort selection
How to select a cohort for each claimed speaker.
Feature design
How to fully use these cohort scores.
Discriminative model training
How to build a more powerful decision model.
Add three parts in the typical GMM-UBM system.
1/13/2019
APSIPA ASC 2016

7. Cohort-based decision making framework
Cohort selection
Vector quantization (VQ)
K-L distance
Minimize the within-class cost
stopping criterion
1/13/2019
APSIPA ASC 2016

8. Cohort-based decision making framework
Feature design
Distribution of scores
Score normalization
Rank position
Sorted score differences …
The distribution of scores between target test and imposter test is different.
1/13/2019
APSIPA ASC 2016

9. Experiments
Database (‘CSLT-DSDB’: all recordings is the text-prompted digit strings.)
Training set:
200 females and 200 males for UBM training.
Development set:
145 speakers including 280 enrollment and 2,874 test utterances.
Cohort selection and feature design.
Evaluation set:
92 speakers including 1,220 target trials and 111,020 non-target trials.
Experimental setups
13-dim MFCCs
256 Gaussian components ∆ ∆∆
1/13/2019
APSIPA ASC 2016

10. Experiments Two cohort selection criterion Global clustering
Sibling speakers
Nearest neighbor
1/13/2019
APSIPA ASC 2016

11. Experiments Feature design Rank position Sorted score differences
1/13/2019
APSIPA ASC 2016

12. Experiments Discriminative model training SVM-based scoring
NN-based scoring
1.621 (Baseline)
1.621 (Baseline)
‘norm’ is the standard T-norm methods, ‘r-pos’ is the ‘Rank position’, ‘s-diff’ is the ‘Sorted score differences’.
1/13/2019
APSIPA ASC 2016

13. Conclusions Decision making method Future work
Distribution of Cohort scores
Design score-level features (‘sorted score differences’)
Powerful discriminative models
Stable and better performance than GMM-UBM baseline
Future work
Feature designing and cohort selection
1/13/2019
APSIPA ASC 2016

14. Thank you lilt.cslt.org IEEE APSIPA ASC Dec. 13-16, 2016, Jeju, Korea
1/13/2019
APSIPA ASC 2016