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

Existing Singlish spoken corpora have primarily focused on linguistic analysis and speech recognition Speech technologies have evolved over decades, tasks (Deterding and Low, 2001; Chen et al., progressing from modularized solutions for speech 2010; Lyu et al., 2010; Tan, 2019). Given the relatively recognition (Povey et al., 2011; Radford et al., small population of Singlish speakers, estimated 2023), speaker identification (Togneri and Pullella, at just a few million, resources for Singlish 2011), and gender recognition (Hechmi et al., speech corpora are significantly more limited compared 2021) with modularized toolkits like Kaldi (Povey to major languages like English, Chinese, et al., 2011) and ESPnet (Watanabe et al., 2018) French, and Spanish. Singapore's government to advanced solutions integrating large language agency, IMDA, has open-sourced the largest available models for multimodal understanding in an allencompassing, Singlish corpus, known as the National Speech omni-style approach (Team et al., Corpus (Koh et al., 2019).