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Shi Wang

13 papers hereh-index 5256 citations14 works total

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

author position
  • first author6
  • middle author6

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.SD6
  • cs.CL3
  • eess.AS3
  • cs.AI1
same name
  • Shi Wang — 13 papers, h 5
  • Shi Wang — 7 papers, h 23
  • Shi Wang — 6 papers, h 7
  • Shi Wang — 5 papers, h 5
  • Shi Wang — 4 papers, h 1
  • Shi Wang — 4 papers, h 3

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
20222026
most citedHow to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario

1 citations · 1 across the 12 of their papers we have counts for

collaborators
Showing 2026 · cs.SDShow all

3 papers · 2 filters

cs.SD2026

Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: Exploration toward Age, Gender, and Accent Steering

Shih-Heng Wang, Tiantian Feng, Aditya Kommineni +4

Neural audio codecs (NACs) are widely used in speech generation and audio-language modeling, yet how they encode speaker-trait information remains poorly understood. Prior work app…

cs.SD2026

Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: A Case Study on Accent Information

Shih-Heng Wang, Tiantian Feng, Aditya Kommineni +4

Neural Audio Codecs (NACs) are widely adopted in modern speech systems, yet how they encode linguistic and paralinguistic information remains unclear. Improving the interpretabilit…

cs.SD2026

Do Neural Codecs Generalize? A Controlled Study Across Unseen Languages and Non-Speech Tasks

Shih-Heng Wang, Jiatong Shi, Jinchuan Tian +2

This paper investigates three crucial yet underexplored aspects of the generalization capabilities of neural audio codecs (NACs): (i) whether NACs can generalize to unseen language…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.