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Ting Dang

23 papers hereh-index 6134 citations27 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author14
  • last author7

Across the 23 of 23 papers where every author was matched, so the position is known.

fields
  • cs.AI5
  • cs.LG5
  • cs.SD4
  • cs.CL3
  • cs.HC3
  • cs.CV1
same name
  • Ting Dang — 19 papers, h 3
  • Ting Dang — 10 papers, h 2
  • Ting Dang — 6 papers, h 2
  • Ting Dang — 2 papers, h 0
  • Ting Dang — 1 paper, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing cs.SDShow all

4 papers · 1 filter

cs.SD2026

Edge-Cloud Collaborative Speech Emotion Captioning via Token-Level Speculative Decoding in Audio-Language Models

Xiangyuan Xue, Jiajun Lu, Yan Gao +3

Speech Emotion Captioning (SEC) leverages large audio-language models to generate rich, context-aware affective descriptions from speech. However, real-world deployment remains cha…

cs.SD2026

Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction

Xiaofeng Yu, Jiaheng Dong, Jean Honorio +3

Speech emotion recognition plays an important role in various applications. However, most existing approaches predict a single emotion label, oversimplifying the inherently ambiguo…

cs.SD2025

Token-Level Logits Matter: A Closer Look at Speech Foundation Models for Ambiguous Emotion Recognition

Jule Valendo Halim, Siyi Wang, Hong Jia +1

Emotional intelligence in conversational AI is crucial across domains like human-computer interaction. While numerous models have been developed, they often overlook the complexity…

cs.SD2025

Scaling Auditory Cognition via Test-Time Compute in Audio Language Models

Ting Dang, Yan Gao, Hong Jia

Large language models (LLMs) have shown exceptional versatility in natural language processing, prompting recent efforts to extend their multimodal capabilities to speech processin…

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