I am now a first-year PhD candidate at Centre for Language Studies (CLS) , Radboud University, supervised by M.A. Larson (Martha), co-supervised by C. Tejedor Garcia (Cristian), and L.F.M. ten Bosch (Louis). Before that, I was a visiting researcher at the Audio Information Research(AIR) Lab of University of Rochester. I used to be a senior researcher at Tencent AI Lab, AI-Generated Content (AIGC) Center for speech synthesis applied research.

My research interest include but not limit to Speech diagnostic for neurodegenerative disease, Responsible AI, and Spoken Dialogue System.

🔥 News

2024.07: Becoming a PhD Candidate at Centre for Language Studies, Radboud University
2024.05: One paper is accepted by INTERSPEECH 2024
2023.12: One paper is accepted by ICASSP 2024 (Oral)
2023.05: One paper is accepted by INTERSPEECH 2023

📖 Educations

2024 - *, PhD Candidate, Centre for Language Studies, Radboud University
2016 - 2018, M.Sc in Computer Science, Rutgers University–New Brunswick, NJ, USA.
2011 - 2015, B.Sc in Information and Computing Science, Beijing Jiaotong University, Beijing, China.

2024.7 -, PhD Candidate, Centre for Language Studies, Radboud University
2023.7 - 2023.10, Visiting Researcher, Audio Information Research(AIR) Lab, University of Rochester
2021 - 2023.7, Senior Researcher, AI-Generated Content (AIGC) Center, Tencent AI Lab
2018 - 2021, Machine Learning Engineer, 17 Education&Tech Group Inc. (IPO on NASDAQ)
2017 - 2018, Machine Learning Engineer Intern, Learnable.ai

Review Committee: ICASSP 2024, ISMIR 2023-2024

INTERSPEECH 2023

EE-TTS: Emphatic Expressive TTS with Linguistic Information
Yi Zhong, Chen Zhang, Xule Liu, Chenxi Sun, Weishan Deng, Haifeng Hu, Zhongqian Sun. [Demo Page]

Contribution:

EE-TTS can identify appropriate emphasis positions from text and synthesize expressive speech with emphasis and linguistic information.
This work outperforms baseline with expressiveness-MOS improvements from 3.76 to 4.25 and naturalness-MOS from 3.67 to 4.34.
EE-TTS helps build AI playmate services for the world’s most-played mobile MOBA game Honor of Kings (DAU 100+ million).

Yongyi Zang*, Yi Zhong*, Frank Cwitkowitz, Zhiyao Duan. [Demo Page]

Zehua Kcriss Li, Meiying Melissa Chen, *Yi Zhong*, Pinxin Liu, Zhiyao Duan. [Demo Page]

Few-shot Voice Cloning and Style Transfer

Achieved few-shot voice cloning using 20 utterances. The Similarity-MOS of timbre reached 4.6 with a MOS of 3.8 and a clear pronunciation correction effect on L2 English speakers.[patented]
Pre-train and finetune paradigm and frame-level pitch modeling are used to achieve few-shot style transfer using 20 utterances. The style SMOS has been improved from 3.5 to 4.5 while naturalness MOS remains above 4.0.

Probabilistic Topic Models Based Music Recommendation System supervisor: Vladimir Pavlovic

Leveraged CRNN for music tagging, and exploited Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) probabilistic topic models for music topic modelling.
Use KL divergence to compute the similarity of song-topic distributions for the recommendation.

Recognition and Evaluation of Oral English

Design and optimize the Goodness Of Pronunciation (GOP) feature, implementation, and tuning of LR, XGBoost, LSTM classifiers. Attained SOTA English oral evaluation consistency rate. [patented]
Full pipeline chain-model training and optimization based on Kaldi framework, including corpus crawling, language and acoustic model training, Bi-RNN implementation, RNN-Rescore, etc.
Achieved 5%-10% WER on various benchmark datasets and outperformed Google ASR API on children datasets.

Voice Conversion Timbre Similarity Improvement

Method: Optimized the bottleneck of hidden representation for an any-to-one PPG-pipeline VC system. [patented]
Result: Improved the similarity MOS of voice timbre from 3.9 to 4.3.
Implemented many-to-many VC models such as VQ-VAE, StarGAN-VC for comparison.