Bayesian Distance Metric Learning

This thesis explores the use of Bayesian distance metric learning (Bayes-dml) for the task of speaker verification using the i-vector feature representation. We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. With an approximation of the distance metric as a weighted covariance matrix of the top eigenvectors from the data covariance matrix, variational inference is used to estimate a posterior distribution of the distance metric. Given speaker labels, we select different-speaker data pairs with the highest cosine scores to form a different-speaker constraint set. This set captures the most discriminative between-speaker variability that exists in the training data. This system is evaluated on the female part of the 2008 NIST SRE dataset. Cosine similarity scoring, as the state-of-the-art approach, is compared to Bayes-dml. Experimental results show the comparable performance between Bayes-dml and cosine similarity scoring. Furthermore, Bayes-dml is insensitive to score normalization, as compared to cosine similarity scoring. Without the requirement of the number of labeled examples, Bayes-dml performs better in the context of limited training data. Thesis Supervisor: James R. Glass Title: Senior Research Scientist Thesis Supervisor: Najim Dehak Title: Research Scientist Acknowledgments First and foremost, I would like to thank Jim Glass and Najim Dehak for offering me the opportunity to do research in their group. I am grateful to Jim for his consideration and patience all the time, for always guiding me down the right path. I appreciate Najim's passion and brilliance. Najim's broad knowledge and thoughtful insights in this field have inspired me a lot through this thesis work. This thesis would not have been possible without them. The Spoken Language Systems group has provided me a research home in the past two years. Thank you to all the members for your energy, creativity, and friendship. The birthday celebrations, the spectrum reading seminars, and the defense suit-ups would be unforgettable memories in my life. I would like to thank all my friends, old and new, locally available and geographically separated, for accompanying and encouraging me, for sharing tear and joy with me. Lastly I would like to thank my parents and my elder brother for their love, inspiration, and support all these years. This thesis was supported by DARPA under the RATS (Robust Automatic Transcription of Speech) Program.