# SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia

arXiv: https://arxiv.org/abs/2511.01670 · HTML: https://mlsanity.com/paper/2511.01670/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming](https://mlsanity.com/paper/2408.16725/) (2024)
- [SeaLLMs -- Large Language Models for Southeast Asia](https://mlsanity.com/paper/2312.00738/) (2023)
- [YODAS: Youtube-Oriented Dataset for Audio and Speech](https://mlsanity.com/paper/2406.00899/) (2024)
- [Distilling an End-to-End Voice Assistant Without Instruction Training Data](https://mlsanity.com/paper/2410.02678/) (2024)
- [SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages](https://mlsanity.com/paper/2407.19672/) (2024)
- [GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement](https://mlsanity.com/paper/2406.11546/) (2024)
- [Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs](https://mlsanity.com/paper/2502.12982/) (2025)
- [Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers](https://mlsanity.com/paper/2503.00865/) (2025)
- [SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia](https://mlsanity.com/paper/2502.06298/) (2025)

## Built on by (most cited first, 8 of 8)

- [Membership Inference Attacks against Large Audio Language Models](https://mlsanity.com/paper/2603.28378/) (2026)
- [Language-Aware Distillation for Multilingual Instruction-Following Speech LLMs with ASR-Only Supervision](https://mlsanity.com/paper/2603.07025/) (2026)
- [MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages](https://mlsanity.com/paper/2511.04914/) (2025)
- [RedVox: Safety and Fairness Gaps in Speech Models Across Languages](https://mlsanity.com/paper/2606.26968/) (2026)
- [Efficiently Adapting Spoken Language Models for the Singaporean Context](https://mlsanity.com/paper/2607.10092/) (2026)
- [SEA-SpeechBench: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia](https://mlsanity.com/paper/2609.09672/) (2026)
- [SEABED: SouthEast Asian Benchmark for Evaluating Audio Reasoning](https://mlsanity.com/paper/2609.22586/) (2026)
- [Audio Token Attention Is Predictable Before the Language Model Runs](https://mlsanity.com/paper/2609.38878/) (2026)

## Abstract (arXiv)

We introduce SeaLLMs-Audio, the first large audio-language model (LALM) tailored for multiple Southeast Asian (SEA) languages-Indonesian (id), Thai (th), and Vietnamese (vi)-alongside English (en) and Chinese (zh). Trained on a large-scale audio corpus, SeaLLMs-Audio exhibits strong performance across diverse audio-centric tasks, spanning fine-grained audio understanding and voice-based interaction. Its key features include: 1) Multilingual: the model primarily supports 5 languages, namely Indonesian, Thai, Vietnamese, English, and Chinese; 2) Multimodal: the model accepts flexible input modalities, including audio only, text only, as well as audio with text; 3) Multi-task: the model supports a wide range of tasks, including audio analysis tasks such as Audio Captioning, Automatic Speech Recognition, Speech-to-Text Translation, Speech Emotion Recognition, Speech Question Answering, and Speech Summarization. It also enables voice-based dialogue, including answering factual, mathematical, and general knowledge queries. As a significant step towards advancing audio LLMs in Southeast Asia, we expect SeaLLMs-Audio to benefit both the regional research community and industry. To automate LALM evaluation for Southeast Asia, we introduce SeaBench-Audio, a benchmark spanning multiple tasks. Experiments show that SeaLLMs-Audio achieves competitive performance compared with other LALMs on SEA languages.