SEA-SpeechBench is a large‑scale multitask benchmark for speech understanding in 11 Southeast Asian languages, comprising 97,194 samples across 99 evaluation sets and 597 hours of curated audio. It covers nine tasks in three categories—speech processing, paralinguistic analysis, and a novel temporal understanding dimension—using multilingual prompting in both native SEA languages and English. Evaluation of current models shows significant performance gaps, especially in temporal understanding, emotion recognition, and speech translation, with low‑resource languages lagging behind English by up to 41 percentage points.
By Jingyi Liao, Wenyu Zhang, Zhuohan Liu, Yingxu He, Geyu Lin, Xunlong Zou, Shuo Sun, Syed Ali Redha Alsagoff, Ai Ti Aw
arXiv:2609.22586v1 Announce Type: cross
Abstract: Modern audio-language models are no longer judged only on what words they can transcribe, but on whether they can reason over what they hear: recover...
By Harshit Rajgarhia, Asif Shaik, Rachuri Lokesh, Sushanta Kumar Pani, Abhishek Mukherji
arXiv:2610.19869v1 Announce Type: cross
Abstract: We introduce SEA-HELM Audio, a Southeast Asian (SEA) multilingual evaluation framework that extends the text-based SEA-HELM benchmark to assess perfo...
By Yixian Tan, Aulia Adila, Jonathan Heng, Kunat Pipatanakul, Donghang Wu, Anocha Sutaveephamochanon
arXiv:2604.19782v2 Announce Type: replace-cross
Abstract: Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LAL...
By Jinyoung Kim, Hyeongsoo Lim, Eunseo Seo, Minho Jang, Keunwoo Choi, Seungyoun Shin, Ji Won Yoon
arXiv:2608. 04433v1 Announce Type: cross Abstract: We present MERaLiON-GR, a speech gender recognition system that performs binary classification (female / male) on English and Southeast Asian (SEA) languages.
By Qiongqiong Wang, Ai Ti Aw, Nancy F. Chen, Ying Lay Chiu, Yang Ding, Yingxu He, Ridong Jiang, Zhuohan Liu, Yanfeng Lu, Yi Ma, Muhammad Huzaifah, Nabilah Binte Md Johan, Nattadaporn Lertcheva, Pham Minh Duc, Sailor Hardik Bhupendra, Siti Umairah Binte Mohammad Salleh, Shuo Sun, Tarun Kumar Vangani, Jeremy H. M. Wong, Jinyang Wu, Longyin Zhang
arXiv:2606. 01016v1 Announce Type: cross Abstract: While End-to-End (E2E) Speech-Large Language Models (Speech-LLMs) are rapidly evolving, their evaluation methodologies remain limited to the era of simple transcription.
By Sicheng Yang, Shulan Ruan, Shiwei Wu, Yu Liu, Lu Fan, Zhi Li, You He
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
arXiv:2610.19388v1 Announce Type: new
Abstract: Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresen...
By Sangmin Lee, Yejin Jeon, David Ifeoluwa Adelani
arXiv:2609.36920v1 Announce Type: new
Abstract: Comprehensive evaluations of automatic speech recognition (ASR) for Iberian languages remain limited, and low-resource languages, biases, and efficienc...
By Fernando L\'opez, Pablo G\'omez, David Solans, Paulo Villegas, Jordi Luque
arXiv:2609.35820v1 Announce Type: cross
Abstract: English-only benchmarks expose only a narrow slice of voice-agent behavior. We introduce $\tau$-Multilingual, extending $\tau$-Voice to Spanish, Braz...
By Soham Ray, Edgard dos Santos Paiva, Ruben Valenzuela, Karthik Narasimhan, Keshav Dhandhania, Victor Barres
arXiv:2609.14991v1 Announce Type: new
Abstract: Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling ou...
By Warit Sirichotedumrong, Tanawin Samutsin, Shah Faisal Wani, Sittipong Sripaisarnmongkol, Kunat Pipatanakul
arXiv:2607. 10092v1 Announce Type: cross Abstract: Spoken language models (SLMs) unify speech perception and reasoning, but adapting them to sensitive domains is underexplored, especially when the original training data is inaccessible and the use case demands multilingual, spoken-query interaction.
By Ng Jia Sheng Jason