4 citations · 6 across the 16 of their papers we have counts for
16 papers
SEA-SpeechBench: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia
Jingyi Liao, Wenyu Zhang, Zhuohan Liu +6
The rapid advancement of audio and multimodal large language models has unlocked transformative speech understanding capabilities, yet evaluation frameworks remain predominantly En…
Direct Preference Optimization for English-Mandarin Code-Switching Speech Recognition in Audio LLMs
Trung Nguyen Quang, Cheng Yi Lewis Won, Minh Duc Pham +3
Audio large language models (Audio LLMs) exhibit systematic failures in transcribing code-switching speech despite strong multilingual capabilities. Focusing on English-Mandarin, w…
Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance
Weihua Zheng, Chang Liu, Zhengyuan Liu +5
Multilingual Large Language Models (LLMs) struggle with cross-lingual tasks due to data imbalances between high-resource and low-resource languages, as well as monolingual bias in…
Unlocking Cognitive Capabilities and Analyzing the Perception-Logic Trade-off
Longyin Zhang, Shuo Sun, Yingxu He +15
Recent advancements in Multimodal Large Language Models (MLLMs) pursue omni-perception capabilities, yet integrating robust sensory grounding with complex reasoning remains a chall…
MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages
Hardik B. Sailor, Aw Ai Ti, Chen Fang Yih Nancy +26
We present MERaLiON-SER, a robust speech emotion recognition model designed for English and Southeast Asian languages. The model is trained using a hybrid objective combining weigh…
Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data
Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu +7
Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech cruc…
Incorporating Contextual Paralinguistic Understanding in Large Speech-Language Models
Qiongqiong Wang, Hardik B. Sailor, Jeremy H. M. Wong +6
Current large speech language models (Speech-LLMs) often exhibit limitations in empathetic reasoning, primarily due to the absence of training datasets that integrate both contextu…
Contextual Paralinguistic Data Creation for Multi-Modal Speech-LLM: Data Condensation and Spoken QA Generation
Qiongqiong Wang, Hardik B. Sailor, Tianchi Liu +1
Current speech-LLMs exhibit limited capability in contextual reasoning alongside paralinguistic understanding, primarily due to the lack of Question-Answer (QA) datasets that cover…
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs
Wenyu Zhang, Yingxu He, Geyu Lin +9
Audio Large Language Models (AudioLLMs) have achieved strong results in semantic tasks like speech recognition and translation, but remain limited in modeling paralinguistic cues s…
IFEval-Audio: Benchmarking Instruction-Following Capability in Audio-based Large Language Models
Yiming Gao, Bin Wang, Chengwei Wei +2
Large language models (LLMs) have demonstrated strong instruction-following capabilities in text-based tasks. However, this ability often deteriorates in multimodal models after al…
MERaLiON-TextLLM: Cross-Lingual Understanding of Large Language Models in Chinese, Indonesian, Malay, and Singlish
Xin Huang, Tarun Kumar Vangani, Minh Duc Pham +4
Multilingual large language models (MLLMs) have shown impressive capabilities across a variety of languages. However, efficacy can differ greatly between different language familie…
MERaLiON-AudioLLM: Bridging Audio and Language with Large Language Models
Yingxu He, Zhuohan Liu, Shuo Sun +5
We introduce MERaLiON-AudioLLM (Multimodal Empathetic Reasoning and Learning in One Network), the first speech-text model tailored for Singapore's multilingual and multicultural la…
Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models
Bin Wang, Xunlong Zou, Shuo Sun +6
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,…
MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond
Muhammad Huzaifah, Geyu Lin, Tianchi Liu +8
This technical report describes the MERaLiON-SpeechEncoder, a foundation model designed to support a wide range of downstream speech applications. Developed as part of Singapore's…
MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoders
Wenyu Zhang, Shuo Sun, Bin Wang +6
The rapid advancements in large language models (LLMs) have significantly enhanced natural language processing capabilities, facilitating the development of AudioLLMs that process…
AudioBench: A Universal Benchmark for Audio Large Language Models
Bin Wang, Xunlong Zou, Geyu Lin +6
We introduce AudioBench, a universal benchmark designed to evaluate Audio Large Language Models (AudioLLMs). It encompasses 8 distinct tasks and 26 datasets, among which, 7 are new…