arXiv (All)
open access
Audiovisual joint learning for end-to-end hearing aids
Speech understanding in noise remains challenging for hearing-aid users, particularly in the presence of competing speakers. Conventional hearing aids typically perform speech enhancement (SE) and hearing-loss compensation in separate stages, which may cause enhancement errors and signal distortions
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
- Speech Intelligibility in Quiet and Noise for New vs. Legacy Hearing Aids#1046561
- Evaluation of Novel Noise Reduction Principles in Hearing Instruments#969860
- DualSpecSE: A Dual-Path Speech Enhancement Network Integrating Mel and Complex Spectrograms#1083404
- The Effect of Deep Neural Network Implementation on Speech Recognition, Listening Effort, and Sound Quality in Older Adults With Mild to Moderately Severe Hearing Loss.#195995
- Computer-Based Auditory Rehabilitation#980546
Record · ID 1218314
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