Tianxin Xie

I am a PhD student at the AI Thrust, Information Hub, Hong Kong University of Science and Technology (Guangzhou), advised by Prof. Li Liu. I received my B.E. degree in Computer Science from Hunan Normal University, and my M.S. degree in Computer Science from the University of Chinese Academy of Sciences, advised by Prof. Hu Han.

My research focuses on text-to-speech, speech LLMs, audio generation and reasoning, and multi-modal learning.

Email: txie151[at]connect.hkust-gz.edu.cn / tianxin.xie[at]outlook.com

Google Scholar

ORCID

GitHub

news

Apr 07, 2026	We have released a T2AV benchmark, i.e., PhyAVBench, which contains 11,605 newly recorded videos. The dataset will be soon released to the public.
Jan 27, 2026	One paper accepted by ICLR 2026.
Nov 10, 2025	I have started my internship at Tencent AI Lab.
Nov 08, 2025	One paper accepted by AAAI 2026.
Aug 21, 2025	One paper accepted by EMNLP 2025.
Dec 21, 2024	One paper accepted by ICASSP 2025.
Dec 13, 2024	One paper accepted by TPAMI.
Dec 09, 2024	We released a comprehensive survey of controllable speech synthesis on arXiv.
Jul 01, 2024	I graduated from the Institute of Computing Technology, Chinese Academy of Sciences, and will be pursuing my PhD at HKUST (GZ).

latest posts

selected publications

arXiv

PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation

Tianxin Xie, Wentao Lei, Kai Jiang, and 8 more authors

arXiv preprint arXiv:2512.23994, 2025

Bib PDF

@article{xie2025phyavbench,
  title = {PhyAVBench: A Challenging Audio Physics-Sensitivity Benchmark for Physically Grounded Text-to-Audio-Video Generation},
  author = {Xie, Tianxin and Lei, Wentao and Jiang, Kai and Huang, Guanjie and Zhang, Pengfei and Zhang, Chunhui and Ma, Fengji and He, Haoyu and Zhang, Han and He, Jiangshan and others},
  journal = {arXiv preprint arXiv:2512.23994},
  year = {2025},
}

EMNLP 2025
Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey

Tianxin Xie, Yan Rong, Pengfei Zhang, and 2 more authors

In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Abs DOI Bib PDF Code

Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over attributes like emotion, timbre, and style. Driven by rising industrial demand and breakthroughs in deep learning, e.g., diffusion and large language models (LLMs), controllable TTS has become a rapidly growing research area. This survey provides **the first** comprehensive review of controllable TTS methods, from traditional control techniques to emerging approaches using natural language prompts. We categorize model architectures, control strategies, and feature representations, while also summarizing challenges, datasets, and evaluations in controllable TTS. This survey aims to guide researchers and practitioners by offering a clear taxonomy and highlighting future directions in this fast-evolving field. One can visit https://github.com/imxtx/awesome-controllabe-speech-synthesis for a comprehensive paper list and updates.
@inproceedings{xie-etal-2025-towards, title = {Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey}, author = {Xie, Tianxin and Rong, Yan and Zhang, Pengfei and Wang, Wenwu and Liu, Li}, editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet}, booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing}, year = {2025}, address = {Suzhou, China}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2025.emnlp-main.40/}, doi = {10.18653/v1/2025.emnlp-main.40}, pages = {764--791}, isbn = {979-8-89176-332-6}, }

arXiv

EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering

Tianxin Xie, Shan Yang, Chenxing Li, and 2 more authors

arXiv preprint arXiv:2508.03543, 2025

Bib PDF

@article{xie2025emosteer,
  title = {EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering},
  author = {Xie, Tianxin and Yang, Shan and Li, Chenxing and Yu, Dong and Liu, Li},
  journal = {arXiv preprint arXiv:2508.03543},
  year = {2025},
}

ICASSP 2025

Inter- and Intra-Sentence Cuer-Invariant Representation Learning for Generalizable Cued Speech Recognition

Tianxin Xie and Li Liu

In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025

DOI Bib PDF

@inproceedings{xie2025icasspcued,
  title = {Inter- and Intra-Sentence Cuer-Invariant Representation Learning for Generalizable Cued Speech Recognition},
  author = {Xie, Tianxin and Liu, Li},
  booktitle = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year = {2025},
  pages = {1--5},
  keywords = {Hands; Representation learning; Degradation; Visualization; Lips; Speech recognition; Contrastive learning; Feature extraction; Synchronization; Speech processing; Cued Speech Recognition; Multi-modal Learning; Cuer Generalization; Contrastive Learning},
  doi = {10.1109/ICASSP49660.2025.10888246},
}

TPAMI

Natural Adversarial Mask for Face Identity Protection in Physical World

Tianxin Xie, Hu Han, Shiguang Shan, and 1 more author

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

DOI Bib PDF Code

@article{xie2025tpami,
  author = {Xie, Tianxin and Han, Hu and Shan, Shiguang and Chen, Xilin},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title = {Natural Adversarial Mask for Face Identity Protection in Physical World},
  year = {2025},
  volume = {47},
  number = {3},
  pages = {2089-2106},
  keywords = {Face recognition;Faces;Protection;Closed box;Privacy;Glass box;Perturbation methods;Three-dimensional printing;Target recognition;Face masks;Deep learning;adversarial example;face identity protection;physical world;natural face mask},
  doi = {10.1109/TPAMI.2024.3522994},
}