SPEAR: A Unified SSL Framework for Learning Speech and Audio Representations
Abstract
Self-Supervised Learning (SSL) excels at learning generic representations of acoustic signals, yet prevailing methods remain domain-specific, tailored to either speech or general audio, hindering the development of a unified representation model with a comprehensive capability over both domains. To address this, we present SPEAR (SPEech and Audio Representations), the first SSL framework to successfully learn unified speech and audio representations from a mixture of speech and audio data. SPEAR proposes a unified pre-training objective based on masked prediction of fine-grained discrete tokens for both speech and general audio. These tokens are derived from continuous speech and audio representations using a Multi-codebook Vector Quantisation (MVQ) method, retaining rich acoustic detail essential for modelling both speech and complex audio events. SPEAR is applied to pre-train both single-domain and unified speech-and-audio SSL models. Our speech-domain model establishes a new state-of-the-art on the SUPERB benchmark, a speech processing benchmark for SSL models, matching or surpassing the highly competitive WavLM Large on 12 out of 15 tasks with the same pre-training corpora and a similar model size. Crucially, our unified model learns complementary features and demonstrates comprehensive capabilities across two major benchmarks, SUPERB and HEAR, for evaluating audio representations. By further scaling up the model size and pre-training data, we present a unified model with 600M parameters that excels in both domains, establishing it as one of the most powerful and versatile open-source SSL models for auditory understanding. The inference code and pre-trained models will be made publicly available.
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
The main contribution of this paper is the introduction of SPEAR, a unified self-supervised learning framework that effectively learns representations for both speech and audio, achieving state-of-the-art results and demonstrating comprehensive capabilities across multiple benchmarks. This work represents a significant advancement in the field of audio representation learning, addressing a critical need for models that can operate across different acoustic domains.
Methodology
The methodology presented in SPEAR is robust and innovative, focusing on a unified self-supervised learning framework that integrates speech and audio representation learning. The use of masked prediction of fine-grained discrete tokens derived from continuous representations via Multi-codebook Vector Quantisation (MVQ) is particularly noteworthy, as it retains essential acoustic details that are crucial for both speech and audio processing. This approach addresses a significant gap in the existing literature where models are typically domain-specific. The framework's ability to pre-train models effectively across both domains is a substantial contribution to the field.
Results & evaluation
The experimental setup is comprehensive, with the authors validating their models on established benchmarks such as SUPERB and HEAR. The results indicate that the speech-domain model achieves state-of-the-art performance, surpassing competitive models like WavLM Large. The unified model also demonstrates complementary feature learning, which is a critical aspect of its design. However, the paper could benefit from a more detailed analysis of the datasets used and the specific metrics for evaluation.
Reproducibility
While the authors mention that the inference code and pre-trained models will be made publicly available, the paper lacks detailed implementation specifics that would facilitate reproducibility. Clear documentation of the training process, hyperparameters, and data preprocessing steps would enhance the reproducibility of the results.
Limitations
One limitation of the study is the potential overfitting to the benchmarks used for evaluation, as the results are primarily reported on a limited set of tasks. Additionally, the scalability of the model with respect to diverse audio datasets outside the tested benchmarks remains to be explored. The paper could also address the computational resources required for training such large models, which may limit accessibility for some researchers.
Broader impact
The SPEAR framework has significant implications for various applications in speech and audio processing, including voice recognition, audio event detection, and multimedia content analysis. By providing a unified model, it opens avenues for cross-domain applications and could lead to advancements in areas such as human-computer interaction and accessibility technologies. The main contribution of this paper is the introduction of SPEAR, a unified self-supervised learning framework that effectively learns representations for both speech and audio, achieving state-of-the-art results and demonstrating comprehensive capabilities across multiple benchmarks. This work represents a significant advancement in the field of audio representation learning, addressing a critical need for models that can operate across different acoustic domains.
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