Zewang Zhang

Orcid: 0009-0001-3620-8706

According to our database1, Zewang Zhang authored at least 12 papers between 2016 and 2023.

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

Timeline

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PhD thesis 
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Links

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Bibliography

2023
WeSinger 2: Fully Parallel Singing Voice Synthesis via Multi-Singer Conditional Adversarial Training.
Proceedings of the IEEE International Conference on Acoustics, 2023

A multi-dimensional information sensing and monitoring system based on wearable devices.
Proceedings of the 2023 International Conference on AI and Metaverse in Supply Chain Management, 2023

2022
WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses.
Proceedings of the 23rd Annual Conference of the International Speech Communication Association, 2022

Zero-Shot Cross-Lingual Transfer Using Multi-Stream Encoder and Efficient Speaker Representation.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
FeatherTTS: Robust and Efficient attention based Neural TTS.
Proceedings of the 11th ISCA Speech Synthesis Workshop, 2021

2020
AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN.
CoRR, 2020

FeatherWave: An Efficient High-Fidelity Neural Vocoder with Multi-Band Linear Prediction.
Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

The Tencent speech synthesis system for Blizzard Challenge 2020.
Proceedings of the Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 2020

2018
Data Compression Scheme Based on Discrete Sine Transform and Lloyd-Max Quantization.
Proceedings of the 3rd International Conference on Intelligent Information Processing, 2018

Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding.
Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2018

2016
An Experimental Comparison of Deep Neural Networks for End-to-end Speech Recognition.
CoRR, 2016

Grammar Argumented LSTM Neural Networks with Note-Level Encoding for Music Composition.
CoRR, 2016


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