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

Autor:	Wang, Bin, Zou, Xunlong, Sun, Shuo, Zhang, Wenyu, He, Yingxu, Liu, Zhuohan, Wei, Chengwei, Chen, Nancy F., Aw, AiTi
Rok vydání:	2025
Předmět:	Computer Science - Computation and Language Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Open-Source: https://github.com/AudioLLMs/Singlish
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2501.01034 Zobrazit plný text záznamu View this record from Arxiv