A Large-Scale Evaluation of Speech Foundation Models (2404.09385v2)

Abstract: The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling and data annotation. This approach has proven crucial in the field of NLP. However, the speech processing community lacks a similar setup to explore the paradigm systematically. In this work, we establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the paradigm for speech. We propose a unified multi-tasking framework to address speech processing tasks in SUPERB using a frozen foundation model followed by task-specialized, lightweight prediction heads. Combining our results with community submissions, we verify that the foundation model paradigm is promising for speech, and our multi-tasking framework is simple yet effective, as the best-performing foundation model shows competitive generalizability across most SUPERB tasks. For reproducibility and extensibility, we have developed a long-term maintained platform that enables deterministic benchmarking, allows for result sharing via an online leaderboard, and promotes collaboration through a community-driven benchmark database to support new development cycles. Finally, we conduct a series of analyses to offer an in-depth understanding of SUPERB and speech foundation models, including information flows across tasks inside the models, the correctness of the weighted-sum benchmarking protocol and the statistical significance and robustness of the benchmark.

• The paper demonstrates that speech foundation models using self-supervised learning achieve competitive performance on a wide range of 15 speech tasks.
• The evaluation employs a robust methodology where a frozen model and lightweight task-specific heads isolate the transferability of pre-trained representations.
• The results highlight that while SSL models excel in discriminative tasks, they underperform in generative tasks like speech enhancement and source separation.

A Comprehensive Evaluation of Speech Foundation Models Using the SUPERB Framework

Introduction

The Speech processing Universal PERformance Benchmark (SUPERB) is introduced as a systematic framework for evaluating speech foundation models akin to similar paradigms in NLP and computer vision. By engaging a unified multi-task framework with SUPERB, researchers can validate and compare the effectiveness of various speech foundation models across a comprehensive set of 15 speech processing tasks, ranging from phoneme recognition to voice conversion.

Methodology and Framework

SUPERB benchmarks speech foundation models using a simple yet robust methodology. A frozen foundation model serves inputs into task-specific, lightweight prediction heads that are the only components trained during the benchmarking process. This approach ensures a focus on evaluating the transferability and generality of the pre-trained representations across diverse tasks. Tasks within SUPERB include phoneme recognition, keyword spotting, speaker identification, and more, covering a broad spectrum of speech processing applications.

Results and Findings

Results demonstrate that the models leveraging self-supervised learning (SSL) techniques show promising task generalizability across most SUPERB tasks, often equating or surpassing the performance of specialized task-specific models. Top-performing models were able to achieve significant performance markers without the need to fine-tune the core parameters of the speech foundation model, relying instead on the optimization of task-specific heads.

1. Task Generalizability: Most models outperformed a standard FBANK baseline across tasks. SSL techniques particularly excel in direct usability on real applications, suggesting a robust generalization capability.
2. SSL Model Performance: Leading SSL models like WavLM and HuBERT demonstrated exceptional generalizability by delivering competitive or superior performance across a myriad of tasks in comparison to traditional non-SSL models.
3. Performance on Generative Tasks: While SSL models showed promise on understanding and discriminative tasks, they lagged behind specially designed models for generative tasks such as speech enhancement (SE) and source separation (SS) indicating a potential area for future enhancement of SSL methodologies.

Analysis of Results

• Layer-wise Performance: It was observed that not all layers contribute equally to tasks. Lower layers better served generative tasks like SE, while higher layers were more effective for discriminative tasks like phoneme recognition.
• Layer Weights and Performance: Contrary to expectations, weights assigned to layers after benchmarking did not reliably indicate the importance of layers for specific tasks, suggesting that such weights might not be suitable for interpreting model behavior or for distinguishing which layers are more task-critical.
• Challenge of Benchmarking Voice Conversion: The benchmarking process indicated that the best-performing models for voice conversion tasks are not necessarily consistent with those leading in other areas, largely owed to the specific requirement for speaker-independent representations.

Practical Implications and Community Involvement

The introduction of an online platform and leaderboard as part of SUPERB has facilitated an active community around SSL in speech processing, enabling sharing, reproducibility, and iterative refinement of models. This community-driven benchmark is pivotal, not only for validation but also for pushing forward the development cycle of speech processing technologies.

Future Directions

Future research may focus on enhancing the performance of SSL techniques on generative tasks, refining benchmarking protocols (possibly including layer-wise performance analysis specifically for tasks like voice conversion), and investigating more robust models that manage distortion or low-resource conditions effectively. The ongoing refinement of the SUPERB benchmark, community contributions, and multi-probe evaluation strategies will play critical roles in addressing these challenges.

In conclusion, the SUPERB benchmark represents a significant stride towards establishing a robust framework for assessing the efficacy of speech foundation models across a broad range of tasks. It provides valuable insights into the capabilities and limitations of current SSL technologies, and the foundations laid out by this benchmark are expected to significantly guide future research in speech processing.