{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dynamic-superb-phase-2-a-collaboratively","title":"Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks","arxiv_id":"2411.05361","date":"2024-11-08","proceeding":null,"authors":["Chien-yu Huang","Wei-Chih Chen","Shu-wen Yang","Andy T. Liu","Chen-An Li","Yu-Xiang Lin","Wei-Cheng Tseng","Anuj Diwan","Yi-Jen Shih","Jiatong Shi","William Chen","Chih-Kai Yang","Wenze Ren","Xuanjun Chen","Chi-Yuan Hsiao","Puyuan Peng","Shih-Heng Wang","Chun-Yi Kuan","Ke-Han Lu","Kai-Wei Chang","Fabian Ritter-Gutierrez","Kuan-Po Huang","Siddhant Arora","You-Kuan Lin","Ming To Chuang","Eunjung Yeo","Kalvin Chang","Chung-Ming Chien","Kwanghee Choi","Jun-You Wang","Cheng-Hsiu Hsieh","Yi-Cheng Lin","Chee-En Yu","I-Hsiang Chiu","Heitor R. Guimarães","Jionghao Han","Tzu-Quan Lin","Tzu-Yuan Lin","Homu Chang","Ting-Wu Chang","Chun Wei Chen","Shou-Jen Chen","Yu-Hua Chen","Hsi-Chun Cheng","Kunal Dhawan","Jia-Lin Fang","Shi-Xin Fang","Kuan-Yu Fang Chiang","Chi An Fu","Hsien-Fu Hsiao","Ching Yu Hsu","Shao-Syuan Huang","Lee Chen Wei","Hsi-Che Lin","Hsuan-Hao Lin","Hsuan-Ting Lin","Jian-Ren Lin","Ting-Chun Liu","Li-Chun Lu","Tsung-Min Pai","Ankita Pasad","Shih-Yun Shan Kuan","Suwon Shon","Yuxun Tang","Yun-Shao Tsai","Jui-Chiang Wei","Tzu-Chieh Wei","Chengxi Wu","Dien-Ruei Wu","Chao-Han Huck Yang","Chieh-Chi Yang","Jia Qi Yip","Shao-Xiang Yuan","Vahid Noroozi","Zhehuai Chen","Haibin Wu","Karen Livescu","David Harwath","Shinji Watanabe","Hung-Yi Lee"],"abstract":"Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language instructions is critical for bridging communication gaps and facilitating more intuitive interactions. However, the absence of a comprehensive evaluation benchmark poses a significant challenge. We present Dynamic-SUPERB Phase-2, an open and evolving benchmark for the comprehensive evaluation of instruction-based universal speech models. Building upon the first generation, this second version incorporates 125 new tasks contributed collaboratively by the global research community, expanding the benchmark to a total of 180 tasks, making it the largest benchmark for speech and audio evaluation. While the first generation of Dynamic-SUPERB was limited to classification tasks, Dynamic-SUPERB Phase-2 broadens its evaluation capabilities by introducing a wide array of novel and diverse tasks, including regression and sequence generation, across speech, music, and environmental audio. Evaluation results show that no model performed well universally. SALMONN-13B excelled in English ASR and Qwen2-Audio-7B-Instruct showed high accuracy in emotion recognition, but current models still require further innovations to handle a broader range of tasks. We open-source all task data and the evaluation pipeline at https://github.com/dynamic-superb/dynamic-superb.","url_abs":"https://arxiv.org/abs/2411.05361v2","url_pdf":"https://arxiv.org/pdf/2411.05361v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dynamic-superb-phase-2-a-collaboratively","repo_url":"https://github.com/dynamic-superb/dynamic-superb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2411.05361","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.05361"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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