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·· 22 小时前
基于语音激活映射（SPAM）的语音单元分割与识别
Phone Segmentation and Recognition through Phonological Activation Mapping
AI 导读
研究者提出基于自监督语音模型（S3M）的语音激活映射（SPAM），将每个 S3M 表征帧映射为清浊、鼻音等音系特征激活向量，并在其上引入识别头和分割头两个轻量、免梯度下降的预测头，仅需不到一分钟的音标转写即可同时完成语音单元分割与识别，识别头还能泛化到训练中未见的音素。该方法在多个数据集上取得较强的分割性能，已被 SLT 2026 接收。
正文
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