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arXiv · 2501.01034

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

Bin Wang·Xunlong Zou·Shuo Sun·Wenyu Zhang·Yingxu He·Zhuohan Liu·Chengwei Wei·Nancy F. Chen·AiTi Aw
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Abstract

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.

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Bin Wang, Xunlong Zou, Shuo Sun, Wenyu Zhang, Yingxu He, Zhuohan Liu, Chengwei Wei, Nancy F. Chen, AiTi Aw. 2025-01-02. Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models. https://arxiv.org/abs/2501.01034

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