# Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models

arXiv: https://arxiv.org/abs/2501.01034 · HTML: https://mlsanity.com/paper/2501.01034/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2025.

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

- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [Incorporating Contextual Paralinguistic Understanding in Large Speech-Language Models](https://mlsanity.com/paper/2508.07273/) (2025)
- [Unlocking Cognitive Capabilities and Analyzing the Perception-Logic Trade-off](https://mlsanity.com/paper/2602.23730/) (2026)
- [FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation](https://mlsanity.com/paper/2601.06199/) (2026)
- [Convex Low-resource Accent-Robust Language Detection in Speech Recognition](https://mlsanity.com/paper/2605.23235/) (2026)
- [ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots](https://mlsanity.com/paper/2606.18319/) (2026)
- [Efficiently Adapting Spoken Language Models for the Singaporean Context](https://mlsanity.com/paper/2607.10092/) (2026)
- [MERaLiON-GR: Speech Gender Recognition Model for English and SEA Languages](https://mlsanity.com/paper/2608.04433/) (2026)

## Abstract (arXiv)

Singlish, a Creole language rooted in English, is a key focus in linguistic research within multilingual and multicultural contexts. However, its spoken form remains underexplored, limiting insights into its linguistic structure and applications. To address this gap, we standardize and annotate the largest spoken Singlish corpus, introducing the Multitask National Speech Corpus (MNSC). These datasets support diverse tasks, including Automatic Speech Recognition (ASR), Spoken Question Answering (SQA), Spoken Dialogue Summarization (SDS), and Paralinguistic Question Answering (PQA). We release standardized splits and a human-verified test set to facilitate further research. Additionally, we propose SingAudioLLM, a multi-task multimodal model leveraging multimodal large language models to handle these tasks concurrently. Experiments reveal our models adaptability to Singlish context, achieving state-of-the-art performance and outperforming prior models by 10-30% in comparison with other AudioLLMs and cascaded solutions.