SUPERB: Speech processing Universal PERformance Benchmark (2105.01051v4)

Abstract: Self-supervised learning (SSL) has proven vital for advancing research in NLP and computer vision (CV). The paradigm pretrains a shared model on large volumes of unlabeled data and achieves state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the speech processing community lacks a similar setup to systematically explore the paradigm. To bridge this gap, we introduce Speech processing Universal PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. Among multiple usages of the shared model, we especially focus on extracting the representation learned from SSL due to its preferable re-usability. We present a simple framework to solve SUPERB tasks by learning task-specialized lightweight prediction heads on top of the frozen shared model. Our results demonstrate that the framework is promising as SSL representations show competitive generalizability and accessibility across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a benchmark toolkit to fuel the research in representation learning and general speech processing.

Insights into SUPERB: Speech Processing Universal PERformance Benchmark

The research paper titled "SUPERB: Speech processing Universal PERformance Benchmark" introduces a significant contribution to the field of self-supervised learning (SSL) for speech processing. Authored by Shu-wen Yang et al., this paper presents a framework designed to systematically benchmark the performance of SSL models across a variety of speech processing tasks. The paper details the evaluation structure, the underlying models, and the results obtained from this benchmarking exercise.

Overview

Self-supervised learning has seen substantial success in domains such as NLP and computer vision (CV). However, the speech processing community has not yet adopted a standardized benchmark akin to GLUE for NLP or VISSL for CV. The SUPERB framework seeks to fill this gap by providing a comprehensive leaderboard to evaluate SSL models in speech processing. Specifically, it assesses the generalizability and re-usability of pretrained models across ten diverse speech-related tasks with minimal architecture adjustments. These tasks span several aspects of speech processing, including content recognition, speaker identification, semantic understanding, and paralinguistics.

Benchmarking Methodology

SUPERB focuses on evaluating a range of SSL models by extracting representations from these models and applying lightweight, task-specific prediction heads on top of the frozen shared models. This approach leverages SSL's capability to encode general-purpose knowledge from large corpora of unlabeled data, significantly reducing the resources needed for task-specific training.

Tasks

The ten tasks in the SUPERB benchmark are designed to cover a broad spectrum of speech processing:

• Content: Phoneme Recognition (PR), Automatic Speech Recognition (ASR), Keyword Spotting (KS), and Query-by-Example Spoken Term Detection (QbE)
• Speaker: Speaker Identification (SID), Automatic Speaker Verification (ASV), and Speaker Diarization (SD)
• Semantics: Intent Classification (IC) and Slot Filling (SF)
• Paralinguistics: Emotion Recognition (ER)

These tasks are chosen based on conventional evaluation protocols and publicly available datasets, ensuring that they are reproducible and accessible to the research community.

SSL Models

The paper evaluates several SSL models categorized into three learning approaches:

1. Generative Modeling: Includes models like APC, VQ-APC, and DeCoAR 2.0, which focus on reconstructing future frames or masked inputs.
2. Discriminative Modeling: Encompasses models such as CPC, wav2vec, and HuBERT, which rely on contrastive learning or token prediction.
3. Multi-task Learning: Illustrated by PASE+, which integrates multiple pretraining objectives.

Key Results

The performance of different SSL models on the various tasks is presented comprehensively. Some notable outcomes include:

• wav2vec 2.0 and HuBERT achieve strong performance across most tasks, especially in Phoneme Recognition (PR) and Intent Classification (IC) with just linear models, showcasing their robust feature extraction capabilities.
• HuBERT yields the highest performance in Query-by-Example Spoken Term Detection (QbE) and outperforms traditional supervised features like phoneme posteriorgrams (PPGs).
• The gap between SSL representations and traditional features like FBANK is substantially large in tasks like Automatic Speech Recognition (ASR) and Slot Filling (SF).

Implications and Future Directions

The research illustrates that while SSL models exhibit a high degree of generalizability, there are still challenges in terms of adapting these models to a few specific tasks like Speaker Diarization (SD) and Automatic Speaker Verification (ASV). The findings encourage further exploration into more adaptive and versatile SSL models that can cater to the nuanced needs of each task.

Looking forward, SUPERB provides a pivotal platform for advancing SSL research in speech processing. Its open-sourced benchmark toolkit and leaderboard create an ecosystem for continuous improvement and innovation. Future research can leverage this benchmark to develop more efficient models and investigate hybrid approaches that combine generative, discriminative, and multi-task learning paradigms.

Conclusion

The introduction of SUPERB marks a significant milestone for benchmarking SSL models in speech processing. By offering a uniform evaluation platform, it sets the stage for more structured and comparative research, fostering advancements that can democratize deep learning capabilities across various speech processing applications. Researchers are encouraged to participate and contribute to this collaborative effort, driving the boundaries of what SSL models can achieve in the field of speech processing.