POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
Authors: name: Chin-Jou Li, name: Kalvin Chang, name: Shikhar Bharadwaj, name: Eunjung Yeo, name: Kwanghee Choi, name: Jian Zhu, name: David Mortensen, name: Shinji Watanabe
Organization: CMU-LTI

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.