WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Abstract
Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre-trained models are available at https://aka.ms/wavlm.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
WavLM represents a significant advancement in self-supervised learning for speech processing, demonstrating the ability to generalize across multiple tasks while effectively handling complex acoustic environments. The combination of innovative methodologies and extensive experimental validation positions this work as a valuable contribution to the field of machine learning and audio processing.
Methodology
The paper introduces WavLM, a self-supervised learning model for speech processing that combines masked speech prediction and denoising tasks. The methodology is robust, leveraging a large-scale dataset of 94k hours to train the model, which enhances its generalization across multiple speech tasks. The incorporation of gated relative position bias in the Transformer architecture is a notable innovation that improves the model's ability to capture sequential information in speech data. The masked speech denoising task is particularly significant as it allows the model to learn from noisy and overlapping speech, which is crucial for real-world applications.
Results & evaluation
The experiments conducted are extensive, covering nineteen subtasks, including speaker verification, speech separation, and diarization, with WavLM achieving state-of-the-art results on the SUPERB benchmark. The results demonstrate significant improvements over existing models like HuBERT and wav2vec 2.0, indicating the effectiveness of the proposed methods. The evaluation metrics used are appropriate and comprehensive, providing a clear picture of the model's performance across various tasks.
Reproducibility
The paper provides sufficient details regarding the model architecture, training procedures, and datasets used, which facilitates reproducibility. However, the absence of a demo URL for interactive exploration of the model limits immediate accessibility for other researchers.
Limitations
One limitation is the reliance on large-scale unlabeled datasets, which may not always be available for other languages or dialects. Additionally, while the model shows great promise in various tasks, the performance on specific niche tasks may require further tuning or additional data. The paper also does not address the computational cost associated with training such large models, which could be a barrier for some researchers.
Broader impact
The advancements made by WavLM have the potential to significantly impact various applications in speech processing, including virtual assistants, transcription services, and accessibility tools for the hearing impaired. By improving the robustness of speech models in noisy environments, this research could enhance user experiences in real-world applications. WavLM represents a significant advancement in self-supervised learning for speech processing, demonstrating the ability to generalize across multiple tasks while effectively handling complex acoustic environments. The combination of innovative methodologies and extensive experimental validation positions this work as a valuable contribution to the field of machine learning and audio processing.
Curator note
Chen et al., Microsoft; denoising + masked prediction; best self-supervised speech model for years
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