Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech

• URL: http://arxiv.org/abs/2309.09510v2
• Date: Fri, 22 Mar 2024 15:25:04 GMT
• Title: Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech
• Authors: Chien-yu Huang, Ke-Han Lu, Shih-Heng Wang, Chi-Yuan Hsiao, Chun-Yi Kuan, Haibin Wu, Siddhant Arora, Kai-Wei Chang, Jiatong Shi, Yifan Peng, Roshan Sharma, Shinji Watanabe, Bhiksha Ramakrishnan, Shady Shehata, Hung-yi Lee,
• Abstract summary: Text language models have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. We present Dynamic-SUPERB, a benchmark for building universal speech models capable of leveraging instruction tuning to perform multiple tasks in a zero-shot fashion.
• Score: 107.81472531864195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text language models 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 language models, 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.

Related papers

• Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks [112.7791602217381]
  We present Dynamic-SUPERB Phase-2, an open benchmark for the comprehensive evaluation of instruction-based universal speech models. Building upon the first generation, this second version incorporates 125 new tasks, expanding the benchmark to a total of 180 tasks. Evaluation results indicate that none of the models performed well universally.
  arXiv  Detail & Related papers  (2024-11-08T06:33:22Z)
• SpeechVerse: A Large-scale Generalizable Audio Language Model [38.67969337605572]
  SpeechVerse is a robust multi-task training and curriculum learning framework. It combines pre-trained speech and text foundation models via a small set of learnable parameters. Our empirical experiments reveal that our multi-task SpeechVerse model is even superior to conventional task-specific baselines on 9 out of the 11 tasks.
  arXiv  Detail & Related papers  (2024-05-14T03:33:31Z)
• A Large-Scale Evaluation of Speech Foundation Models [110.95827399522204]
  We establish the Speech processing Universal PERformance Benchmark (SUPERB) to study the effectiveness of the foundation model 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.
  arXiv  Detail & Related papers  (2024-04-15T00:03:16Z)
• WavLLM: Towards Robust and Adaptive Speech Large Language Model [93.0773293897888]
  We introduce WavLLM, a robust and adaptive speech large language model with dual encoders, and a prompt-aware LoRA weight adapter. We validate the proposed model on universal speech benchmarks including tasks such as ASR, ST, SV, ER, and also apply it to specialized datasets like Gaokao English listening comprehension set for SQA, and speech Chain-of-Thought (CoT) evaluation set.
  arXiv  Detail & Related papers  (2024-03-31T12:01:32Z)
• SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks [88.4408774253634]
  Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community. There are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers. Recent work has begun to introduce such benchmark for several tasks.
  arXiv  Detail & Related papers  (2022-12-20T18:39:59Z)
• Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks [95.06087720086133]
  Natural-Instructions v2 is a collection of 1,600+ diverse language tasks and their expert written instructions. The benchmark covers 70+ distinct task types, such as tagging, in-filling, and rewriting. This benchmark enables large-scale evaluation of cross-task generalization of the models.
  arXiv  Detail & Related papers  (2022-04-16T03:12:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.