arXiv (All)
open access
BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR
Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR). In this paper, we propose BaLEEN (Biasing with Latent Encoded Entities), a lightweight, hypernetwork-based framework for dynamic contextual adaptation without fine-tuning the
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
- Inference-Time Target Speaker Unlearning in LLM-Based Automatic Speech Recognition#1104638
- ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions#1069855
- SpeechAnnotator: A Context-Aware Multi-Agent Framework and Benchmark for Multidimensional Speech Annotation#996758
- Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts#819785
- Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts#809789
Record · ID 1132114
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