ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers (2204.09224v2)

Abstract: Self-supervised learning in speech involves training a speech representation network on a large-scale unannotated speech corpus, and then applying the learned representations to downstream tasks. Since the majority of the downstream tasks of SSL learning in speech largely focus on the content information in speech, the most desirable speech representations should be able to disentangle unwanted variations, such as speaker variations, from the content. However, disentangling speakers is very challenging, because removing the speaker information could easily result in a loss of content as well, and the damage of the latter usually far outweighs the benefit of the former. In this paper, we propose a new SSL method that can achieve speaker disentanglement without severe loss of content. Our approach is adapted from the HuBERT framework, and incorporates disentangling mechanisms to regularize both the teacher labels and the learned representations. We evaluate the benefit of speaker disentanglement on a set of content-related downstream tasks, and observe a consistent and notable performance advantage of our speaker-disentangled representations.

• The paper presents a CONTENTVEC model that improves self-supervised speech representations by effectively disentangling speaker characteristics from speech content.
• The methodology employs unsupervised voice conversion for teacher label generation and a SimCLR-inspired contrastive loss for student training to reduce speaker dependency.
• Empirical evaluations show CONTENTVEC outperforms conventional HuBERT on key speech benchmarks, enhancing content-specific tasks and minimizing speaker-specific interference.

Enhanced Self-Supervised Speech Representation: A Leap in Speaker Disentanglement

The paper "CONTENTVEC: An Improved Self-Supervised Speech Representation by Disentangling Speakers" addresses a fundamental issue in self-supervised learning (SSL) for speech processing—separating speaker characteristics from speech content during the feature extraction process. This research adapts the HuBERT framework to incorporate mechanisms that better manage this disentanglement, thereby improving the quality of speech representations for downstream tasks.

Technical Context and Innovations

The authors begin by situating their work within the landscape of self-supervised speech learning. Traditionally, SSL techniques like HuBERT rely on masked prediction tasks to derive meaningful speech representations from large unannotated datasets. However, these methods often retain both content and speaker-specific information, which complicates tasks primarily targeting content understanding.

The primary innovation in this paper is the CONTENTVEC model, which integrates three key mechanisms for enhanced speaker disentanglement:

1. Disentanglement in Teachers: A process that converts all training utterances to a canonical speaker voice using an unsupervised voice conversion model before generating teacher labels. This ensures that speaker variation is minimized in the target labels used for training.
2. Disentanglement in Students: This involves using a contrastive loss mechanism inspired by SimCLR to penalize differences in representations of the same content spoken by different speakers. By applying random transformations that specifically alter speaker identity without affecting content, the model enforces representation invariance to speaker variations.
3. Speaker Conditioning: By introducing speaker embeddings into the predictor during the masked prediction task, the need for the representations to encode speaker information is significantly reduced, allowing the model to focus on capturing content.

These strategies collectively result in a superior separation of speaker identity from speech content, as substantiated by comprehensive evaluations across multiple speech processing benchmarks.

Empirical Evaluations

The paper extensively evaluates CONTENTVEC using both zero-shot content probing tasks from the Zero-Resource Speech Challenge and various tasks from the SUPERB benchmark. Notable improvements in tasks such as phonetic classification, keyword spotting, and intent classification were achieved, demonstrating the effectiveness of speaker disentanglement for content-specific applications. Particularly, CONTENTVEC outperforms conventional HuBERT and even its iterative variant where the same pretrained model is used without voice conversion steps.

Moreover, CONTENTVEC also shows superior results in reducing speaker identification and accent classification accuracy, indicating effective speaker information removal. This performance is also validated in a challenging setting of voice conversion, where CONTENTVEC-based embeddings achieved higher speaker similarity in synthesized speech.

Implications and Prospective Research Directions

This paper's findings underscore the importance of disentanglement mechanisms in improving speech representation for content-focused applications. From a theoretical perspective, it paves the way for future SSL systems that can flexibly balance content and speaker information across a broader range of speech processing tasks. Practically, the advancements can enhance voice conversion systems, improve ASR models in speaker-variant environments, and enable more diverse voice synthesis applications.

Future research can explore refining the disentanglement mechanisms to preserve even finer content details without affecting the disentanglement quality or increase the robustness against noisier datasets. Further, evaluating the generalizability of these improvements in more diverse speech processing tasks and LLMs could expand the impact of CONTENTVEC.

Overall, the introduction of CONTENTVEC offers a significant methodological contribution to the field of self-supervised speech learning, providing tools to navigate the intricate task of disentangling speakers from content effectively.