POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
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, code and models are released to foster open
science.
6902c05e72739622ee92a66b
Chin-Jou Li
6902c05e72739622ee92a66c
Kalvin Chang
6902c05e72739622ee92a66d
Shikhar Bharadwaj
claimed_verified
2025-11-03T20:57:16.726Z
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/avatars/5fd4330e956faf81230565de7b3de75f.svg
Shikhar Bharadwaj
user
shikhar7ssu
6902c05e72739622ee92a66e
Eunjung Yeo
6902c05e72739622ee92a66f
Kwanghee Choi
6902c05e72739622ee92a670
Jian Zhu
6902c05e72739622ee92a671
David Mortensen
6902c05e72739622ee92a672
Shinji Watanabe
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
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, code and models are released to foster open
science.
A unified framework, POWSM, performs multiple phonetic tasks including ASR, PR, G2P, and P2G, outperforming specialized models while using a single architecture.
automatic speech recognition phone recognition grapheme-to-phoneme conversion phoneme-to-grapheme conversion Wav2Vec2Phoneme ZIPA