POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
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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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表