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).

We introduce MERaLiON-AudioLLM (Multimodal Empathetic Reasoning and Learning in One Network), the first speech-text model tailored for Singapore's multilingual and multicultural landscape. Developed under the National Large Language Models Funding Initiative, Singapore, MERaLiON-AudioLLM integrates advanced speech and text processing to address the diverse linguistic nuances of local accents and dialects, enhancing accessibility and usability in complex, multilingual environments. Our results demonstrate improvements in both speech recognition and task-specific understanding, positioning MERaLiON-AudioLLM as a pioneering solution for region specific AI applications. We envision this release to set a precedent for future models designed to address localised linguistic and cultural contexts in a global framework.

Automatic image captioning is a promising technique for conveying visual information using natural language. It can benefit various tasks in satellite remote sensing, such as environmental monitoring, resource management, disaster management, etc. However, one of the main challenges in this domain is the lack of large-scale image-caption datasets, as they require a lot of human expertise and effort to create. Recent research on large language models (LLMs) has demonstrated their impressive performance in natural language understanding and generation tasks. Nonetheless, most of them cannot handle images (GPT-3.5, Falcon, Claude, etc.), while conventional captioning models pre-trained on general ground-view images often fail to produce detailed and accurate captions for aerial images (BLIP, GIT, CM3, CM3Leon, etc.). To address this problem, we propose a novel approach: Automatic Remote Sensing Image Captioning (ARSIC) to automatically collect captions for remote sensing images by guiding LLMs to describe their object annotations. We also present a benchmark model that adapts the pre-trained generative image2text model (GIT) to generate high-quality captions for remote-sensing images. Our evaluation demonstrates the effectiveness of our approach for collecting captions for remote sensing images.