Zhen Wan, Chao-Han Huck Yang NVIDIA Research, Jinchuan Tian, Hanrong Ye NVIDIA, Ankita Pasad NVIDIA, Szu-Wei Fu NVIDIA and Microsoft, Arushi Goel NVIDIA Research, Ryo Hachiuma NVIDIA 等 18 位作者
We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is motivated by a crucial yet counterintuitive finding: naively fine-tuning an omni-model on both speech recognition and external sound understanding tasks often degrades performance, as the model can be easily misled by noisy hypotheses. To address this, our framework, Speech-Hands, recasts the problem as an explicit self-reflection decision. This learnable reflection primitive proves effective in preventing the model from being derailed by flawed external candidates. We show that this agentic action mechanism generalizes naturally from speech recognition to complex, multiple-choice audio reasoning. Across the OpenASR leaderboard, which includes seven domain-diverse speech datasets, Speech-Hands consistently outperforms strong baselines by 12.1% WER on the OpenASR benchmark. The model also achieves 77.37% accuracy and high F1 on audio QA decisions, showing robust generalization and reliability across diverse audio question answering datasets. By unifying perception and decision-making, our work offers a practical path toward more reliable and resilient audio intelligence.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Chin-Jou Li School of Computer Science, Carnegie Mellon University, Kalvin Chang UC Berkeley, University of California, Berkeley, Shikhar Bharadwaj School of Computer Science, Carnegie Mellon University, Eunjung Yeo University of Texas at Austin, Kwanghee Choi University of Texas at Austin, Jian Zhu University of British Columbia, David R. Mortensen, Shinji Watanabe Japan Broadcasting Corporation, Tohoku University and Carnegie Mellon University
Recent advances in spoken language processing have led to substantial progress in phonetic tasks such as automatic speech recognition (ASR), phone recognition (PR), grapheme-to-phoneme conversion (G2P), and phoneme-to-grapheme conversion (P2G). Despite their conceptual similarity, these tasks have largely been studied in isolation, each relying on task-specific architectures and datasets. In this paper, we introduce POWSM (Phonetic Open Whisper-style Speech Model), the first unified framework capable of jointly performing multiple phone-related tasks. POWSM enables seamless conversion between audio, text (graphemes), and phones, opening up new possibilities for universal and low-resource speech processing. Our model outperforms or matches specialized PR models of similar size (Wav2Vec2Phoneme and ZIPA) while jointly supporting G2P, P2G, and ASR. Our training data, models, and code are released to foster open science.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Shikhar Bharadwaj School of Computer Science, Carnegie Mellon University, Chin-Jou Li School of Computer Science, Carnegie Mellon University, Yoonjae Kim, Kwanghee Choi University of Texas at Austin, Eunjung Yeo University of Texas at Austin, Ryan Soh-Eun Shim Ludwig-Maximilians-Universität München, Hanyu Zhou, Brendon Boldt School of Computer Science, Carnegie Mellon University 等 16 位作者
Phone recognition (PR) serves as the atomic interface for language-agnostic modeling for cross-lingual speech processing and phonetic analysis. Despite prolonged efforts in developing PR systems, current evaluations only measure surface-level transcription accuracy. We introduce PRiSM, the first open-source benchmark designed to expose blind spots in phonetic perception through intrinsic and extrinsic evaluation of PR systems. PRiSM standardizes transcription-based evaluation and assesses downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. We find that diverse language exposure during training is key to PR performance, encoder-CTC models are the most stable, and specialized PR systems still outperform LALMs. PRiSM releases code, recipes, and datasets to move the field toward multilingual speech models with robust phonetic ability.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Guan-Ting Lin ByteDance Inc., Shih-Yun Shan Kuan National Taiwan University, Jiatong Shi, Kai-Wei Chang Massachusetts Institute of Technology, Siddhant Arora Facebook, Shinji Watanabe Japan Broadcasting Corporation, Tohoku University and Carnegie Mellon University, Hung-yi Lee National Taiwan University
While full-duplex speech agents enable natural, low-latency interaction by speaking and listening simultaneously, their consistency and task performance in multi-turn settings remain underexplored. We introduce Full-Duplex-Bench-v2 (FDB-v2), a streaming framework that integrates with an automated examiner that enforces staged goals under two pacing setups (Fast vs. Slow). FDB-v2 covers four task families—Daily, Correction, Entity Tracking, and Safety—and reports turn-taking fluency, multi-turn instruction following, and task-specific competence. The framework is extensible, supporting both commercial APIs and open-source models. When we test full-duplex systems with FDB-v2, they often get confused when people talk at the same time, struggle to handle corrections smoothly, and sometimes lose track of who or what is being talked about. Through an open-source, standardized streaming protocol and a task set, FDB-v2 makes it easy to extend to new task families, allowing the community to tailor and accelerate evaluation of multi-turn full-duplex systems.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表