W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

Abstract: Motivated by the success of masked language modeling~(MLM) in pre-training natural language processing models, we propose w2v-BERT that explores MLM for self-supervised speech representation learning. w2v-BERT is a framework that combines contrastive learning and MLM, where the former trains the model to discretize input continuous speech signals into a finite set of discriminative speech tokens, and the latter trains the model to learn contextualized speech representations via solving a masked prediction task consuming the discretized tokens. In contrast to existing MLM-based speech pre-training frameworks such as HuBERT, which relies on an iterative re-clustering and re-training process, or vq-wav2vec, which concatenates two separately trained modules, w2v-BERT can be optimized in an end-to-end fashion by solving the two self-supervised tasks~(the contrastive task and MLM) simultaneously. Our experiments show that w2v-BERT achieves competitive results compared to current state-of-the-art pre-trained models on the LibriSpeech benchmarks when using the Libri-Light~60k corpus as the unsupervised data. In particular, when compared to published models such as conformer-based wav2vec~2.0 and HuBERT, our model shows~5\% to~10\% relative WER reduction on the test-clean and test-other subsets. When applied to the Google's Voice Search traffic dataset, w2v-BERT outperforms our internal conformer-based wav2vec~2.0 by more than~30\% relatively.

• The paper presents a novel integration of contrastive learning with masked language modeling to pre-train speech representations end-to-end.
• It demonstrates a 5-10% reduction in word error rate on LibriSpeech and a 30% improvement on real-world datasets compared to existing models.
• The framework streamlines training with a unified architecture that mitigates challenges like codebook collapse and complex token assignment.

An Analysis of w2v-BERT for Self-Supervised Speech Representation Learning

The paper presents a novel framework, w2v-BERT, aimed at enhancing self-supervised speech representation learning. The method combines contrastive learning and masked language modeling (MLM) to devise a more efficient pre-training paradigm for automatic speech recognition (ASR). This approach synthesizes the contrasting elements of wav2vec 2.0, known for its contrastive technique, and BERT's MLM, thus creating a unique path in speech processing.

Highlights of the Methodology

w2v-BERT deviates from conventional methods such as HuBERT or vq-wav2vec by integrating the two learning tasks—discretizing continuous speech and conducting MLM—in a consolidated, end-to-end architecture. This simultaneous optimization allows for more streamlined representation learning compared to frameworks employing iterative processes or concatenating separate modules.

Key to the model is its capacity to utilize a quantized vector space derived through contrastive learning as a target for MLM. This dual-task approach is applied within a robust network architecture comprising a feature encoder, contrastive module, and MLM module. Notably, the integration of conformer blocks provides an advantage over traditional transformer or convolution networks by capturing both local and global audio sequence dependencies.

Experimental Insights and Results

The experiments demonstrate that w2v-BERT delivers competitive speech recognition results. When evaluated on the LibriSpeech benchmarks using the Libri-Light 60k corpus, w2v-BERT achieved a 5% to 10% relative reduction in word error rate (WER) compared to other models such as wav2vec 2.0 and HuBERT. In particular, the performance on Google's Voice Search dataset was markedly impressive, with a reported 30% relative improvement over conformer-based wav2vec 2.0. These numerical results assert the efficacy of the proposed model in both canonical and real-world noisy environments.

Theoretical and Practical Implications

The integration of contrastive learning with MLM in w2v-BERT forms a theoretically novel approach, addressing some limitations seen in previous models like HuBERT that rely on heuristic iterative re-clustering. Practically, this allows for more efficient training regimes and adaptable deployment across diverse acoustic environments. The end-to-end training process mitigates the challenges of codebook collapse and token assignment seen in models using a two-stage process.

The findings of the study carry potential implications for further developments in AI-driven speech applications. As observed, the robust performance in low-resource settings and varied acoustic inputs indicates the versatility of w2v-BERT in real-world speech tasks. Future research could explore optimization of hyperparameters specific to w2v-BERT to enhance its adaptability to different datasets, potentially broadening its application in global speech processing fields.

Future Directions

Looking forward, the authors suggest avenues such as hyperparameter tuning and testing w2v-BERT on low-resource benchmarks like Libri-Light to further validate its effectiveness. Such endeavors may reveal additional strengths and limitations, fostering a deeper understanding of the model's capacity to generalize across speech recognition tasks.

In conclusion, w2v-BERT signifies a thoughtful advancement in the domain of self-supervised learning for ASR, combining foundational elements from state-of-the-art models into an experimentally validated, efficient framework. The method's promising results showcase its potential as a robust solution for enhancing speech processing capabilities in both academic and applied settings.