arXiv (All)
open access
Phone Segmentation and Recognition through Phonological Activation Mapping
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- [b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic#1216243
- Learning phonetic distinctions from speech signals#223471
- Pretrained self-supervised speech models can recognize unseen consonants#1248471
- Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba#1048938
- Speech sounds and features#470222
Record · ID 1325217
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive