We introduce MERaLiON-AudioLLM, the first general-purpose audio-based large language model designed for multitask learning, with a particular focus on Singlish understanding. Trained on 62 million multimodal instruction samples comprising a total of 260k hours of audio, it exhibits strong generalization across a diverse set of tasks, including—but not limited to—automatic speech recognition, spoken question answering, speech translation, and paralinguistic analysis. Our results show significant improvements in local speech recognition and task-specific understanding, making MERaLiON-AudioLLM a leading solution for region-specific AI applications. An interactive demo has been developed to enable user-friendly interactions, supported by a backend with customized caching and load-balancing mechanisms. We benchmark the model across a broad range of multilingual and multitask scenarios, where it demonstrates competitive performance compared to other open-source models. The demo page, model weights and videos are publically accessible.

Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. We propose CP-Bench, a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning the integration of verbal content with non-verbal cues like emotion and prosody. The benchmark includes two curated question answering (QA) datasets requiring both linguistic and empathetic understanding. We evaluate state-of-the-art speech-LLMs from both open and closed-source models and perform a comprehensive analysis across different question types. The top two models were further analyzed under temperature tuning to understand its effect on this task. Our benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent speech-capable LLMs.