Title: SEA-SpeechBench: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia
Primary area: datasets and benchmarks
Keywords: Southeast Asian Languages, Multilingual Speech Benchmark, Audio–language Models

Abstract: The rapid advancement of audio and multimodal large language models has unlocked transformative speech understanding capabilities, yet evaluation frameworks remain predominantly English-centric, leaving Southeast Asian (SEA) languages critically underrepresented. We introduce SEA-SpeechBench, the first large-scale multitask benchmark that evaluates speech understanding in 11 SEA languages through more than 97,000 samples and 597 hours of curated audio data. Our benchmark comprises 9 diverse tasks across 3 categories: speech processing (automatic speech recognition, speech translation, spoken question answering), paralinguistic analysis (emotion, gender, age, speaker recognition), and temporal understanding, a novel dimension featuring timestamped content queries and temporal localization within extended audio sequences up to 3 minutes. We implement multilingual prompting in both native SEA languages and English to reflect user interactions with audio-language models. 
Evaluation of leading open-source and proprietary systems reveals marked performance gaps. Across all models, performance remains underwhelming on temporal reasoning, emotion recognition, and speech translation, with most scores falling below 20. Prompting in low-resource languages such as Burmese, Lao, Tamil, and Khmer lag behind English by over 5%.
Our findings expose critical model limitations and underscore the need for inclusive model development.