The paper introduces Inquesto Score (IS), a protocol that measures voice‑agent reliability by calculating the percentage of calls that reach the caller’s goal without functional failure. IS defines explicit failure events and severity levels, evaluates timing, semantic, and state‑dependent failures using audio, scenario predicates, tool traces, and a pinned open‑model judge, and provides diagnostic views on behavior, acoustic robustness, identity handling, and speaker groups. The authors evaluate IS v0.1 on 30 scenarios, three acoustic conditions, four speaker groups, and 306 calls per agent across 13 configurations, demonstrating that reliable measurement requires evidence beyond transcripts and explicit treatment of deployment conditions.
By Massa Baali, Bhiksha Raj
The paper introduces a reproducible framework that converts existing text-based tool‑calling benchmarks into audio‑based evaluations using text‑to‑speech, speaker variation, and environmental noise, while preserving original annotations. Experiments on seven omni‑modal models show that performance varies strongly by model and task, with notable gaps between text and voice results. The study also includes a stress test, a reference‑free LLM‑as‑judge protocol, and demonstrates that open‑source judges can match proprietary ones, aiding privacy‑preserving evaluation.
By Md Tahmid Rahman Laskar, Xue-Yong Fu, Seyyed Saeed Sarfjoo, Quinten McNamara, Jonas Robertson, Shashi Bhushan TN
arXiv:2609.38867v1 Announce Type: new
Abstract: Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular in...
By Terumi Chiba, Guangzhi Sun, Zheqi Yuan, Chao Zhang
arXiv:2607. 14846v1 Announce Type: cross Abstract: Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken language from its textual representation.
By David Ayllon, Alice Baird, Jeffrey Brooks, Franc Camps-Febrer, Jakub Piotr C{\l}apa, Theo Lebryk, Jens Madsen, Olya Ossipova, Sharath Rao, Hoon Shin, Tigran Soghbatyan, Georg Streich, Rashish Tandon, Panagiotis Tzirakis
The paper evaluates the use of large language models (LLMs) as judges for assessing conversational voice agents, comparing human judgments with GPT‑4.1 and GPT‑5 across telecom and retail interactions. It examines agreement, metric‑level correlations, and consistency across three evaluation configurations (p0, p1, p2) to determine how reliably LLMs can judge conversational quality and safety. The study finds that LLM‑based evaluation can be effective but its reliability varies by metric and configuration, suggesting a hybrid approach where LLMs handle scalable assessment while humans focus on metrics requiring contextual interpretation.
By Anupam Purwar, Shashank Singh, Kritika Srivastava
The paper introduces llmovoice, a middleware that explicitly models voice context for large language model (LLM) serving in voice AI applications. By incorporating speaking rate, background noise, packet loss, and other paralinguistic factors into a bounded context, llmovoice guides the LLM to generate more aligned responses. Experiments show significant reductions in speaking‑rate errors, false interruptions, and model usage costs, especially in long voice sessions.
By Linyi Jiang, Silvery D. Fu, Yifei Zhu
arXiv:2609.24812v1 Announce Type: new
Abstract: Voice provides a natural and immediate interface for AI agents. Many settings in which voice agents could be useful, including meetings, households, an...
By Chenxu Xiong, Dongming Shen, Yuzhi Tang, Wentao Ma, Mu Li, Alex Smola
AU-Harness is an open-source toolkit designed to streamline and standardize the evaluation of Audio Language Models (LALMs). It addresses three key limitations of existing frameworks: slow processing pipelines, lack of multi-turn dialogue support, and absence of a scalable, unified evaluation system. The toolkit achieves a 77% reduction in evaluation time, enabling large-scale studies and providing standardized protocols for fair model comparison and in-depth analysis of audio reasoning capabilities.
By Hoang Nguyen, Sidharth Surapaneni, Akshay Kalkunte, Jash Mehta, Aman Tiwari, Oluwanifemi Bamgbose, Khyati Mahajan, Jash Shah, Shruthan Radhakrishna, Sathwik Tejaswi Madhusudhan, Vikas Yadav, Sai Rajeswar
Scalable Context Orchestration for Serving LLMs Over Voice presents llmovoice, a middleware that explicitly models voice context—including speaking rate, background noise, and packet loss—to guide large language model responses. By constructing a bounded voice context at each turn, llmovoice improves alignment with user preferences and reduces errors, achieving a 52.4% drop in speaking‑rate alignment error and a 0.9% false‑interruption rate under packet loss. In addition, it cuts model usage costs dramatically, lowering per‑turn cost by up to 24.9× while maintaining 98.7% of baseline answer quality in long sessions.
arXiv:2603.16783v2 Announce Type: replace
Abstract: Robust voice agents require exposure to the full diversity of how people interact through speech. However, obtaining enough spoken interactions is...
By Jonggeun Lee, Junseong Pyo, Jeongmin Park, Yohan Jo
arXiv:2608. 10716v1 Announce Type: cross Abstract: Speech-to-speech (S2S) voice agents are increasingly being incorporated into enterprise for customer care and as daily companions for consumers owing to the ease of the conversational modality over text.
By Aryan Vijay Bhosale, Harshit Rajgarhia, Akhil Pothanapalli, Asif Shaik, Abhishek Mukherji, Dinesh Manocha