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20242026
most citedAdvancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models

2 citations · 2 across the 14 of their papers we have counts for

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12 papers · 1 filter

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…