A sticky HDP-HMM with application to speaker diarization (0905.2592v4)

Abstract: We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006) 1566--1581]. Although the basic HDP-HMM tends to over-segment the audio data---creating redundant states and rapidly switching among them---we describe an augmented HDP-HMM that provides effective control over the switching rate. We also show that this augmentation makes it possible to treat emission distributions nonparametrically. To scale the resulting architecture to realistic diarization problems, we develop a sampling algorithm that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence, greatly improving mixing rates. Working with a benchmark NIST data set, we show that our Bayesian nonparametric architecture yields state-of-the-art speaker diarization results.

• The paper introduces a sticky extension to the HDP-HMM that reduces redundant state switching and improves segmentation accuracy.
• It incorporates nonparametric emission distributions via a Dirichlet process mixture to effectively capture the multimodal nature of speaker data.
• The authors present an efficient blocked Gibbs sampling method that achieves competitive diarization error rates on benchmark datasets.

Overview of "A Sticky HDP-HMM with Application to Speaker Diarization"

The paper by Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, and Alan S. Willsky introduces a sophisticated Bayesian nonparametric approach to address the challenge of speaker diarization, which involves segmenting a continuous audio recording into speaker-homogeneous time intervals without prior knowledge of the number of speakers. Building upon the hierarchical Dirichlet process hidden Markov model (HDP-HMM), the authors propose an augmented version termed the "sticky HDP-HMM" to enhance the modeling of temporal state persistence, a critical aspect in detecting speaker changes.

Key Contributions

• Augmentation of HDP-HMM: The sticky HDP-HMM introduces an augmented parameter to the HDP-HMM, which biases the model toward state self-transitions, thereby reducing the unrealistic rapid switching and over-segmentation observed in the basic HDP-HMM. This prevents the tendency to create redundant states and facilitates more accurate speaker segmentation.
• Nonparametric Emission Distributions: By extending their model to handle nonparametric emissions through a Dirichlet process mixture, the sticky HDP-HMM accommodates the inherently multimodal nature of speaker-specific emissions. This is crucial in handling complex, real-world audio data where emission distributions can be highly non-Gaussian.
• Inference and Computation: The paper offers a novel blocked Gibbs sampling technique that utilizes a truncated approximation of the Dirichlet process, enabling efficient posterior inference while leveraging dynamic programming principles like the forward-backward algorithm from traditional HMMs. This advances past methods which suffered from slow mixing rates and computational inefficiency.
• State-of-the-Art Results: The proposed model achieves competitive diarization error rates (DER) on the NIST Rich Transcription evaluation data set, comparable to leading methods like the system developed by the International Computer Science Institute (ICSI). The experiments highlight the sticky HDP-HMM’s superior capability in capturing temporal dynamics and speaker differentiation without pre-defined parameters.

Implications and Future Directions

The sticky HDP-HMM represents a significant advance in algorithmic approaches to complex, dynamically varying data sets like continuous human speech. Its ability to model with minimal a priori constraints suggests broader applicability in domains such as bioinformatics, econometrics, and other fields requiring change-point detection in temporally sequenced data.

Future work could enhance the model's flexibility and applicability by exploring its integration with models that account for more sophisticated temporal dependencies beyond the Markovian assumption. Additionally, developing improved sampling algorithms addressing the scalability and convergence challenges observed in high-dimensional applications would further enhance its utility.

In conclusion, the sticky HDP-HMM not only establishes a powerful framework for speaker diarization but also extends the horizons of Bayesian nonparametric modeling in sequential data analysis, solidifying its importance in both theory and application within the field of machine learning and beyond.