---
title: "Summary: 2026-04-23_17-59-47Z_EvaluationofAutomaticSpeechRecognitionUsingGenerat"
date: 2026-04-23
tags: ['paper', 'research', 'ai']
---
# Summary: 2026-04-23_17-59-47Z_EvaluationofAutomaticSpeechRecognitionUsingGenerat


**Source**: [Original Paper](http://arxiv.org/abs/2604.21928v1)
Saved: 2026-05-08 03:29
Source: 2026-04-23_17-59-47Z_EvaluationofAutomaticSpeechRecognitionUsingGenerat.md
Model: None

---

## Summary
The paper evaluates decoder-based generative language models as tools for automatic speech recognition assessment. It studies hypothesis selection, semantic distance, and error classification, finding that LLM judgments align well with humans and offer a more meaning-aware alternative to WER.

## Semantic links
- [[concepts/papers/2026-06-18_17-47-32Z_HowDoInstructionsShapeSpeech_Cross_Attentio_summary.md|Summary: 2026-06-18_17-47-32Z_HowDoInstructionsShapeSpeech_Cross_AttentionAttrib.md]] — 2 title terms overlap; shared tags: ai, paper, research; 1 backlink

## Key Takeaways
- Uses generative LLMs for both pairwise selection and semantic evaluation.
- Reports 92%–94% agreement with human annotators on hypothesis selection.
- Suggests decoder-based LLM embeddings are competitive with encoder-based ones.


## Context
The work explores alternatives to word error rate for ASR evaluation.

## Implications
Generative LLMs may support more interpretable, semantics-aware ASR metrics.

## Original Reference
- Title: Evaluation of Automatic Speech Recognition Using Generative Large Language Models
- Authors: Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil, Sergio Burdisso, Petr Motlicek, Shiran Liu, Mickael Rouvier, Jane Wottawa, Richard Dufour
- URL: http://arxiv.org/abs/2604.21928v1
- Published: 2026-04-23T17:59:47Z
- Source file: /home/rich/wiki/ai-research/raw/papers/2026-04-23_17-59-47Z_EvaluationofAutomaticSpeechRecognitionUsingGenerat.md

[[raw/papers/2026-04-23_17-59-47Z_EvaluationofAutomaticSpeechRecognitionUsingGenerat.md]]

## Related Concepts

- [[concepts/ai-agents/agentic-workflows-hub.md|Agentic Workflows Hub]]
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- [[concepts/llm-models/llm-models-hub.md|LLM Models Hub]]
- [[concepts/evaluation-benchmarks/evaluation-benchmarks-hub.md|Evaluation Benchmarks Hub]]