index.md

Zexin Cai received the B.S. in Software Engineering from Sun Yet-sen University. He is currently pursuing his Ph.D. degree in Electrical and Computer Engineering at Duke University. His primary research interests include text-to-speech synthesis, voice conversion. Previously, he worked as a research assistant at Duke Kunshan University, where he worked on transfer learning in text-to-speech synthesis and customization of the speech recognition system.

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Publications

• Zexin Cai, Chuxiong Zhang, et al. "From Speaker Verification to Multispeaker Speech Synthesis, Deep Transfer with Feedback Constraint," [Submitted to Interspeech 2020]

• Zexin Cai, Chuxiong Zhang, et al. "The DKU Speech Synthesis System for 2019 Blizzard Challenge." in Blizzard Workshop 2019.

• Zexin Cai, Yaogen Yang, et al. "Polyphone Disambiguation for Mandarin Chinese Using Conditional Neural Network with Multi-level Embedding Features" in INTERSPEECH 2019.

• Zexin Cai, Zhicheng Xu, Ming Li. "F0 Contour Estimation Using Phonetic Feature in Electrolaryngeal Speech Ehancement" in ICASSP 2019.

• Zexin Cai, Qin Xiaoyi, Danwei Cai, et al. "The DKU-JNU-EMA Electromagnetic Articulography Database on Mandarin and Chinese Dialects with Tandem Feature based Acoustic-to-Articulatory Inversion." in ISCSLP 2018.

• Weicheng Cai, Zexin Cai, Xiang Zhang, et al. "A novel learnable dictionary encoding layer for end-to-end language identification" in ICASSP 2018.

• Weicheng Cai, Zexin Cai, Wenbo Liu, et al. "Insights into end-to-end learning scheme for language identification" in ICASSP 2018.

• Danwei Cai, Zexin Cai, Ming Li. "Deep Speaker Embeddings with Convolutional Neural Network on Supervector for Text-Independent Speaker Recognition" in APSIPA ASC 2018.

• Haiwei Wu, Ming Li, Zexin Cai, Haibin Zhong. "Unsupervised query by example spoken term detection using features concatenated with Self-Organizing Map distances" in ISCSLP 2018.

• Jinkun Chen, Weicheng Cai, Danwei Cai, Zexin Cai, et al. "End-to-end Language Identification using NetFV and NetVLAD" in ISCSLP 2018.

Skills

• Programming Languages: Python, C, C++, Shell, JAVA

• Application Tool: Tensorflow, Pytorch, KALDI, Matlab, LaTeX

Course (during Ph.D.)

• ECE 581: RANDOM SIGNALS AND NOISE

• ECE 586: Vector Space Methods with Applications

• ECE 590.10.F19: Machine Learning and Deep Neural Nets

• ECE 590D.01D.S20: Reinforcement Learning

Research & Projects

• multispeaker speech synthesis

• Blizzard Challenge 2020