Shuiyang Mao

Orcid: 0000-0002-9302-185X

According to our database1, Shuiyang Mao authored at least 12 papers between 2018 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
KVPO: ODE-Native GRPO for Autoregressive Video Alignment via KV Semantic Exploration.
CoRR, May, 2026

Forcing-KV: Hybrid KV Cache Compression for Efficient Autoregressive Video Diffusion Models.
CoRR, May, 2026

2025
AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement.
CoRR, November, 2025

SpeechAccentLLM: A Unified Framework for Foreign Accent Conversion and Text to Speech.
CoRR, July, 2025

2022
Enhancing Segment-Based Speech Emotion Recognition by Iterative Self-Learning.
IEEE ACM Trans. Audio Speech Lang. Process., 2022

2021
Enhancing Segment-Based Speech Emotion Recognition by Deep Self-Learning.
CoRR, 2021

2020
EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification.
Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

Emotion Profile Refinery for Speech Emotion Classification.
Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

Advancing Multiple Instance Learning with Attention Modeling for Categorical Speech Emotion Recognition.
Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

2019
Deep Learning of Segment-Level Feature Representation with Multiple Instance Learning for Utterance-Level Speech Emotion Recognition.
Proceedings of the 20th Annual Conference of the International Speech Communication Association, 2019

Revisiting Hidden Markov Models for Speech Emotion Recognition.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
An Effective Discriminative Learning Approach for Emotion-Specific Features Using Deep Neural Networks.
Proceedings of the Neural Information Processing - 25th International Conference, 2018


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