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Junan Li

4 accepted papers

2026

MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark

ICLR 2026poster

Speech inherently contains rich acoustic information that extends far beyond the textual language. In real-world spoken communication, effective interpretation often requires integrating semantic meaning (e.g., content), paralinguistic features (e.g., emotions, speed, pitch) and phonological charact…

Cited by 0SourcecodeScholar
2025

Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs

EMNLP 2025

With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has demonstrated strong capabilities in audio-related processing tasks. However, the performance gap between these two paradi

Cited by 0SourcePDFScholar
2024

Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?

NAACL 2024findings

Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for pote…

Cited by 25SourcePDFScholar
2023

Leveraging Pretrained Representations With Task-Related Keywords for Alzheimer's Disease Detection

ICASSP 2023accepted

With the global population aging rapidly, Alzheimer’s disease (AD) is particularly prominent in older adults, which has an insidious onset and leads to a gradual, irreversible deterioration in cognitive domains (memory, communication, etc.). Speech-based AD detection opens up the possibility of wide…

Cited by 0SourceScholar