Cheng Yu


2022

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Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech
Yang Li | Cheng Yu | Guangzhi Sun | Hua Jiang | Fanglei Sun | Weiqin Zu | Ying Wen | Yang Yang | Jun Wang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems. In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features obtained from both past and future sentences. At inference time, instead of the standard Gaussian distribution used by VAE, CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information, which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody. The performance of CUC-VAE is evaluated via a qualitative listening test for naturalness, intelligibility and quantitative measurements, including word error rates and the standard deviation of prosody attributes. Experimental results on LJ-Speech and LibriTTS data show that the proposed CUC-VAE TTS system improves naturalness and prosody diversity with clear margins.

2016

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Pairwise FastText Classifier for Entity Disambiguation
Cheng Yu | Bing Chu | Rohit Ram | James Aichinger | Lizhen Qu | Hanna Suominen
Proceedings of the Australasian Language Technology Association Workshop 2016

2014

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An Introduction to BLCU Personal Attributes Extraction System
Dong Yu | Cheng Yu | Qin Qu | Gongbo Tang | Chunhua Liu | Yue Tian | Jing Yi
Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing