Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech (2309.09510v2)

Abstract: Text LLMs have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. However, existing studies in speech processing primarily focus on limited or specific tasks. Moreover, the lack of standardized benchmarks hinders a fair comparison across different approaches. Thus, we present Dynamic-SUPERB, a benchmark designed for building universal speech models capable of leveraging instruction tuning to perform multiple tasks in a zero-shot fashion. To achieve comprehensive coverage of diverse speech tasks and harness instruction tuning, we invite the community to collaborate and contribute, facilitating the dynamic growth of the benchmark. To initiate, Dynamic-SUPERB features 55 evaluation instances by combining 33 tasks and 22 datasets. This spans a broad spectrum of dimensions, providing a comprehensive platform for evaluation. Additionally, we propose several approaches to establish benchmark baselines. These include the utilization of speech models, text LLMs, and the multimodal encoder. Evaluation results indicate that while these baselines perform reasonably on seen tasks, they struggle with unseen ones. We release all materials to the public and welcome researchers to collaborate on the project, advancing technologies in the field together.

• The paper presents Dynamic-SUPERB, a comprehensive benchmark integrating 55 evaluation instances from 33 tasks to systematically assess instruction tuning in speech models.
• It introduces baseline approaches like Whisper-LLM and ASR-ChatGPT, revealing strong performance on seen tasks but limited generalization to unseen tasks.
• The benchmark’s dynamic, collaborative design invites community contributions to expand task diversity and drive future advancements in universal speech processing.

Dynamic-SUPERB: Advancing Instruction Tuning in Speech Processing

The paper "Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech" addresses the existing limitations within the speech processing domain, particularly concerning universal benchmarks for instruction-tuning models. As LLMs in text processing demonstrate remarkable generalization abilities in zero-shot learning scenarios, the absence of such standardized benchmarks in speech processing poses a significant challenge for cross-approach comparisons. Dynamic-SUPERB emerges as an innovative benchmark initiative within this context.

Dynamic-SUPERB synthesizes 55 evaluation instances across 33 tasks and 22 datasets, offering a broad spectrum that encompasses content, speaker, semantics, degradation, paralinguistics, and non-speech audio dimensions. The primary aim is to create a dynamic and collaborative benchmark that evolves with community contributions and diversifies task variations over time, facilitating a comprehensive assessment of speech models' generalizability.

The paper introduces several baseline approaches integrating instruction tuning, including BERT-GSLM, Whisper, ImageBind-LLM, Whisper-LLM, and a concatenative ASR-ChatGPT framework. These baselines explore the capabilities of self-supervised, multimodal, and LLMs in understanding and executing instruction-based tasks, highlighting both successes and limitations. Notably, Whisper-LLM and ASR-ChatGPT exhibit strong performances in specific dimensions, indicating potential pathways for future research.

Results show that these models perform well on seen tasks, yet struggle significantly with generalizing to unseen tasks, suggesting limitations in instruction comprehension and task adaptability. This performance discrepancy underscores the importance of a collaborative approach to expand Dynamic-SUPERB's task and dataset coverage. By allowing the community to dynamically contribute new tasks and datasets, the benchmark aims to encapsulate a broader spectrum of speech-processing challenges.

The implications of Dynamic-SUPERB are twofold. Practically, it provides a platform for systematically evaluating instruction-tuning models across multiple speech tasks, potentially accelerating the development of universal speech processing models. Theoretically, it encourages research in enhancing model architectures and training strategies to better align with the diverse and growing demands of speech processing tasks.

In future iterations, the community-driven expansion of Dynamic-SUPERB could significantly contribute to understanding and addressing the nuanced aspects of speech processing. Further research into harmonizing speech and text processing models under instruction tuning paradigms may bridge the gap between the two domains, leading to advancements in model generalization and robustness.

Overall, Dynamic-SUPERB represents a promising advancement in the field of speech processing, setting a foundation for continued exploration and innovation through instruction-tuning frameworks. The project's open-source nature and invitation for community collaboration seek to drive forward the research frontier, broadening the horizons of universal speech models.