Speaker Diarization as a Fully Online Learning Problem in MiniVox (2006.04376v3)

Abstract: We proposed a novel machine learning framework to conduct real-time multi-speaker diarization and recognition without prior registration and pretraining in a fully online learning setting. Our contributions are two-fold. First, we proposed a new benchmark to evaluate the rarely studied fully online speaker diarization problem. We built upon existing datasets of real world utterances to automatically curate MiniVox, an experimental environment which generates infinite configurations of continuous multi-speaker speech stream. Second, we considered the practical problem of online learning with episodically revealed rewards and introduced a solution based on semi-supervised and self-supervised learning methods. Additionally, we provided a workable web-based recognition system which interactively handles the cold start problem of new user's addition by transferring representations of old arms to new ones with an extendable contextual bandit. We demonstrated that our proposed method obtained robust performance in the online MiniVox framework.

• The paper introduces a novel online learning framework for speaker diarization by leveraging the MiniVox benchmark and BerlinUCB algorithm.
• It combines MFCC, CNN embeddings, and clustering techniques to continuously update speaker identities in real-time audio streams.
• Empirical results on VoxCeleb demonstrate robust performance under limited reward feedback, emphasizing its practical application in dynamic environments.

Speaker Diarization as a Fully Online Learning Problem in MiniVox: A Critical Analysis

This paper by Lin and Zhang introduces an innovative approach to conducting real-time multi-speaker diarization and recognition. The authors propose framing speaker diarization as a fully online learning problem and claim to offer significant contributions by curating a new benchmark, MiniVox, and developing a real-time recognition system that addresses cold start issues with new users using an extendable contextual bandit framework.

Key Contributions

The paper presents two primary contributions:

1. MiniVox Benchmark: The authors introduce MiniVox, an environment that transforms existing large-scale speaker identification datasets into continuously varying online audio streams. This benchmark allows for evaluating speaker diarization in an online learning setting, which, as the authors note, has not been extensively studied.
2. Contextual Bandit Approach: The paper redefines diarization as an online learning problem where the agent continuously learns from episodically revealed rewards. The authors employ a variant of the Linear Upper Confidence Bound (LinUCB) algorithm, termed BerlinUCB, which incorporates semi-supervised learning and self-supervised techniques to handle the scenarios where reward feedback is missing.

Methodology

The authors' method combines several processes typically treated separately in traditional diarization tasks:

• Embedding Extraction: Techniques such as Mel Frequency Cepstral Coefficients (MFCC) and Convolutional Neural Network (CNN) embeddings are used to create the feature vectors.
• Clustering and Resegmentation: They integrate clustering techniques (e.g., Kmeans, Gaussian Mixture Models, and Spectral Clustering) and update strategies to enable continuous learning of speaker identities.
• Reward Mechanism: The developed system employs a contextual bandit framework where arms, representing distinct speakers, are expanded as new speakers are identified. The BerlinUCB algorithm handles episodically missing rewards by predicting and self-labeling the data where feasible.

Empirical Evaluation

The empirical results are established using the MiniVox benchmark on the VoxCeleb dataset, assessing performance in scenarios with and without an oracle revealing the number of speakers. Key metrics include Diarization Error Rate (DER) and cumulative reward.

• Without Oracle: The BerlinUCB approach showcases a robust performance against baselines, particularly in cases with lower reward revelation probabilities, demonstrating its capability to adapt to environments with limited supervision.
• With Oracle: The results indicate a complex interplay between the clustering modules and online learning algorithms. While traditional clustering can improve performance with oracle-based knowledge, BerlinUCB's unique architecture tends to perform consistently across varying conditions.

Implications

This research has fundamental implications for real-world speaker diarization systems, particularly in settings where pre-registration and pretraining are infeasible. The authors' approach allows systems to operate with minimal initial data, adapting dynamically as new users are introduced. This adaptability is crucial in diverse acoustic environments where off-the-shelf, pre-trained diarization systems fail due to out-of-distribution challenges.

Furthermore, the application of contextual bandits in diarization extends the understanding of bandit problems into new domains, encouraging the development of more nuanced online learning algorithms that can efficiently deal with sparse, unpredictable feedback.

Future Directions

The paper opens several future research avenues, such as exploring more sophisticated embedding extraction techniques tailored for online learning without extensive pretraining. Additionally, optimizing the management of branched user labels in the contextual bandit framework could lead to more efficient system scaling and robustness to misclassification-induced expansions.

In conclusion, Lin and Zhang's work sets a significant milestone in online learning for speaker diarization, proposing a framework that effectively handles the intricacies of real-time, multi-speaker environments. This research not only advances diarization technology but also enriches the exploration of contextual bandit algorithms in complex, dynamic scenarios.