# Benchmarking LLM Judges for Voice-Agent Evaluation: Reliability, Calibration, and Human Oversight

arXiv: https://arxiv.org/abs/2608.24314 · HTML: https://mlsanity.com/paper/2608.24314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 12 of 12)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge](https://mlsanity.com/paper/2406.07791/) (2024)
- [Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates](https://mlsanity.com/paper/2408.13006/) (2024)
- [Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols](https://mlsanity.com/paper/2006.06110/) (2020)
- [Achieving Reliable Human Assessment of Open-Domain Dialogue Systems](https://mlsanity.com/paper/2203.05899/) (2022)
- [MM-tau-p2: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings](https://mlsanity.com/paper/2603.09643/) (2026)
- [From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences](https://mlsanity.com/paper/2604.10332/) (2026)
- [i-LAVA: Insights on Low Latency Voice-2-Voice Architecture for Agents](https://mlsanity.com/paper/2509.20971/) (2025)
- [FOCAL: A Novel Benchmarking Technique for Multi-modal Agents](https://mlsanity.com/paper/2601.07367/) (2026)
- [Augmenting Human Evaluation with LLM Judges: How Many Human Reviews Do You Need?](https://mlsanity.com/paper/2605.16354/) (2026)
- [(Towards) Scalable Reliable Automated Evaluation with Large Language Models](https://mlsanity.com/paper/2607.28282/) (2026)

## Built on by (most cited first, 1 of 1)

- [MTVA-Bench: Evaluating the Language Model Inside Cascaded Voice Agents](https://mlsanity.com/paper/2609.20152/) (2026)

## Abstract (arXiv)

Evaluating conversational voice agents at scale re- quires reliable assessment methods that capture both observ- able interaction quality and the contextual judgment typically provided by human evaluators. We investigate LLM-as-a-Judge evaluation by comparing human judgments with GPT-4.1 and GPT-5 on telecom and retail voice-agent conversations, across conversational quality and safety dimensions. The same interac- tions are scored under three evaluation configurations, p0, p1, and p2, to test whether automated judgments are sensitive to the evaluation setup and whether observed patterns generalize across configurations and judge models. Beyond aggregate agreement, we examine metric-level correlations, evaluator consistency, and systematic human-LLM disagreement to identify which conver- sational attributes can be judged reliably by automation and which remain sensitive to interpretation and context. Effective voice-agent evaluation is also shaped by pipeline-level factors such as speech generation, streaming, and error propagation across ASR, reasoning, and tool-calling stages, motivating our focus on comparing how human and LLM judges score the same interactions end to end. Our results show that LLM- based evaluation can serve as an effective component of large- scale voice-agent assessment, but that its reliability is metric- and configuration-dependent rather than uniform. This pro- vides an empirical framework for identifying which metrics suit automated evaluation and supports hybrid pipelines in which LLM judges handle scalable assessment while human evaluators remain engaged for metrics that demand contextual interpretation and higher-confidence judgment.