1. Speaker Clustering using MDL Principles Kofi Boakye Stat212A Project December 3, 2003

2. Clustering Problem Seek to identify subpopulations within data set Methods: 1)K-means 2)Mixture model 3)Hierarchical (top-down and bottom-up) Concern: How do we choose the number of clusters? Often this is done heuristically Desire: Automatic procedure for clustering with theoretical basis C1 C2 C3

3. View various clusterings as models and select best model MDL principle serves as a method for model selection For a given parametric model, we have code length: Which is approximated by: (BIC) MDL Motivation

4. The Data Speaker data obtained from ICSI Meeting Corpus Speech parameterized as sequence of feature vectors Common practice in speech and speaker recognition Processing results in features that tend to have little correlation Diagonal covariance matrices assumed Feature vectors contain 40 components

5. Use bottom-up clustering with description length as stopping criterion Model each cluster as multivariate Gaussian Merge two nearest clusters according to symmetrized KL divergence: Initialize and merge clusters at the segment (collection of frames) level Imposed minimum duration constraint of 2 seconds for segments *Assumption: independence of frames Incorrect, but often yields good results The Algorithm

6. Evaluation Evaluating clustering performance can be difficult Metrics: 1)Number of Clusters Problem: Ignores composition 2)Cluster purity Problem: Ignores number of clusters and data points Evaluation requires a combination, of 1), 2), and number of data points

7. Results Meeting Bmr001: 3 Male speakers With the exception of singletons, algorithm correctly clustered speakers Perfect clustering when removed

8. Results Meeting Bmr002: 4 Speakers: 3 male, 1 female Again, with the exception of singletons and duos, speakers are correctly clustered Perfect clustering when removed

9. Results Meeting Bmr009: 6 Speakers: 4 male, 2 female 2 females completely merged 2 males completely merged

10. Conclusions Clustering algorithm works, but not robust to: 1)Outliers 2)Overlap in subpopulations - Becomes of increasing concern as number of speakers increases Common issues in clustering

11. Future Work Different distance metric Different model class Gaussian mixtures? Different choice of speech features

12. Fin