Phone Segmentation and Recognition through Phonological Activation Mapping
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\n<li><strong>What it does</strong>: Solves phone segmentation and phone recognition jointly, instead of modeling them as separate tasks.</li>\n<li><strong>Core idea</strong>: Phonetic structure is already latent inside self-supervised speech model (S3M) representations. We develop a method to use these S3M representations efficiently.</li>\n<li><strong>Method</strong>: Uses SPAM (S3M-based Phonological Activation Mapping), which converts each frame into phonological feature activations like voicing and nasality.</li>\n<li><strong>Architecture</strong>: Two lightweight, gradient-descent-free prediction heads on top of SPAM, one for recognition and another for segmentation.</li>\n<li><strong>Data efficiency</strong>: Requires less than one minute of time-aligned phonetic transcriptions to work.</li>\n<li><strong>Generalization</strong>: Handles phones unseen during training, which is useful for low-resource languages and zero-shot phonetic analysis.</li>\n<li><strong>Results</strong>: SOTA phone segmentation and strong recognition performance across a diverse range of datasets.</li>\n</ul>\n","updatedAt":"2026-07-13T02:51:28.142Z","author":{"_id":"66ccd2fcdba9f642125e3a55","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/66ccd2fcdba9f642125e3a55/hTKwQQahkbpNuGlaRhKp7.jpeg","fullname":"Shikhar Bharadwaj","name":"shikhar7ssu","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8754310607910156},"editors":["shikhar7ssu"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/66ccd2fcdba9f642125e3a55/hTKwQQahkbpNuGlaRhKp7.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.09020","authors":[{"_id":"6a54510ca9d74d6e65bbcbeb","name":"Shikhar Bharadwaj","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbec","name":"Kwanghee Choi","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbed","name":"Stephen McIntosh","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbee","name":"Chin-Jou Li","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbef","name":"Eunjung Yeo","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbf0","name":"Daisuke Saito","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbf1","name":"Nobuaki Minematsu","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbf2","name":"Shinji Watanabe","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbf3","name":"Jian Zhu","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbf4","name":"David Harwath","hidden":false},{"_id":"6a54510ca9d74d6e65bbcbf5","name":"David R. Mortensen","hidden":false}],"publishedAt":"2026-07-10T00:00:00.000Z","submittedOnDailyAt":"2026-07-13T00:00:00.000Z","title":"Phone Segmentation and Recognition through Phonological Activation Mapping","submittedOnDailyBy":{"_id":"66ccd2fcdba9f642125e3a55","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/66ccd2fcdba9f642125e3a55/hTKwQQahkbpNuGlaRhKp7.jpeg","isPro":false,"fullname":"Shikhar Bharadwaj","user":"shikhar7ssu","type":"user","name":"shikhar7ssu"},"summary":"Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the representations of self-supervised speech models (S3Ms), and one only needs to steer them to solve both tasks. We leverage S3M-based Phonological Activation Mapping (SPAM), which maps each S3M representation frame to a vector of phonological feature activations, such as voicing and nasality. On top of SPAM, we introduce two simple but effective lightweight, gradient-descent-free prediction heads: a recognition head and a segmentation head. Our method requires less than a minute of phonetic transcriptions, and generalizes to unseen phones during training. Across a diverse range of datasets, our approach attains strong segmentation and recognition performance.","upvotes":1,"discussionId":"6a54510da9d74d6e65bbcbf6","githubRepo":"https://github.com/stephenmac7/phone-metrics","githubRepoAddedBy":"user","githubStars":1},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"66ccd2fcdba9f642125e3a55","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/66ccd2fcdba9f642125e3a55/hTKwQQahkbpNuGlaRhKp7.jpeg","isPro":false,"fullname":"Shikhar Bharadwaj","user":"shikhar7ssu","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2607/2607.09020.md","query":{}}">
Papers
arxiv:2607.09020
Phone Segmentation and Recognition through Phonological Activation Mapping
Published on Jul 10
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Abstract
Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the representations of self-supervised speech models (S3Ms), and one only needs to steer them to solve both tasks. We leverage S3M-based Phonological Activation Mapping (SPAM), which maps each S3M representation frame to a vector of phonological feature activations, such as voicing and nasality. On top of SPAM, we introduce two simple but effective lightweight, gradient-descent-free prediction heads: a recognition head and a segmentation head. Our method requires less than a minute of phonetic transcriptions, and generalizes to unseen phones during training. Across a diverse range of datasets, our approach attains strong segmentation and recognition performance.
Community
- What it does: Solves phone segmentation and phone recognition jointly, instead of modeling them as separate tasks.
- Core idea: Phonetic structure is already latent inside self-supervised speech model (S3M) representations. We develop a method to use these S3M representations efficiently.
- Method: Uses SPAM (S3M-based Phonological Activation Mapping), which converts each frame into phonological feature activations like voicing and nasality.
- Architecture: Two lightweight, gradient-descent-free prediction heads on top of SPAM, one for recognition and another for segmentation.
- Data efficiency: Requires less than one minute of time-aligned phonetic transcriptions to work.
- Generalization: Handles phones unseen during training, which is useful for low-resource languages and zero-shot phonetic analysis.
- Results: SOTA phone segmentation and strong recognition performance across a diverse range of datasets.
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