Brendon Boldt School of Computer Science, Carnegie Mellon University, David R. Mortensen
We build rule-based emergent language (EL) agents using form-meaning mappings induced from ELs (“morphological phrasebooks”) and test their communicative performance in the EL environment with its neural network agents. This contributes three things: First, it assesses the quality of the morphemes discovered by the induction algorithm in situ, which we find to be effective for communicating in the EL. Second, it allows us to uncover morphosyntactic properties of EL through ablating the algorithms which induce and utilize morphemes, showing that the ELs rely on repetition as well as morpheme ordering to convey meaning. Third, we find that the normalized pointwise mutual information of forms and meanings in the morphemes serves as a metric of compositionality that is more closely correlated with the ability of the phrasebook-agents to “speak” and “hear” an EL than existing metrics such as topographic similarity.
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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
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