arXiv AI By Caleb Rascon

Multi-agent Auditory Scene Analysis: Improved Localization Speed and Robustness by Multi-beamformed Speech Quality Feedback

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arXiv Computation and Language
Sep 10

EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents

arXiv:2605.13841v3 Announce Type: replace-cross Abstract: Voice agents are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses realistic conversatio...

By Tara Bogavelli, Gabrielle Gauthier Melan\c{c}on, Katrina Stankiewicz, Oluwanifemi Bamgbose, Fanny Riols, Hoang H. Nguyen, Raghav Mehndiratta, Lindsay Devon Brin, Joseph Marinier, Hari Subramani, Anil Madamala, Sridhar Krishna Nemala, Srinivas Sunkara
arXiv AI
Sep 1

KVoiceBench, KOpenAudioBench, and KMMAU: Agent-Driven Korean Speech Benchmarks for Evaluating SpeechLMs

The paper introduces KVoiceBench, KOpenAudioBench, and KMMAU—three Korean speech benchmarks created through agent-driven frameworks that adapt existing SpokenQA and ASR resources into Korean SpokenQA and audio understanding tasks. These benchmarks total 12,345 samples and are publicly released to evaluate SpeechLMs beyond English. The authors benchmark eight recent SpeechLMs, revealing significant English‑Korean performance gaps and divergent rankings between SpokenQA and audio understanding, highlighting multilingual weaknesses not apparent in English-only tests.

By Haechan Kim, Seungjun Chung, Inkyu Park, Jihoo Lee, Jonghyun Lee