arXiv cs.AI· Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh, Chin-Jou Li, Eunjung Yeo, Daisuke Saito, Nobuaki Minematsu, Shinji Watanabe, Jian Zhu, David Harwath, David R. Mortensen·· 3 天前AI 评分22
基于音系激活映射的语音音素分割与识别
Phone Segmentation and Recognition through Phonological Activation Mapping
AI 导读
研究者提出 SPAM（基于自监督语音模型的音系激活映射），将 S3M 每帧表示映射为清浊、鼻音等音系特征激活向量，并在其上添加识别头和分割头两个轻量、无需梯度下降的预测头。该方法仅需不到一分钟的音素转写数据，识别头可泛化到训练中未见过的音素，在多个数据集上取得强分割性能。该工作已被 SLT 2026 接收。
正文 · 原文
Authors:Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh, Chin-Jou Li, Eunjung Yeo, Daisuke Saito, Nobuaki Minematsu, Shinji Watanabe, Jian Zhu, David Harwath, David R. Mortensen
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), so 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 the recognition head generalizes to phones unseen during training. Across a diverse range of datasets, our approach attains strong segmentation performance.
| Comments: | Accepted to SLT 2026, source code at this https URL |
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2607.09020 [eess.AS] |
| (or arXiv:2607.09020v2 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09020 arXiv-issued DOI via DataCite |
Submission history
From: Kwanghee Choi [view email]
[v1]
Fri, 10 Jul 2026 01:05:30 UTC (418 KB)
[v2]
Sat, 10 Oct 2026 16:17:55 UTC (424 KB)
来源：arXiv cs.AI · arxiv.org