12 citations
- Beijing Advanced Sciences and Innovation CenterCN2 papers
- Beijing Forestry UniversityCN1 paper
- Beijing University of Posts and TelecommunicationsCN1 paper
- Chinese Academy of SciencesCN1 paper
- Shanghai University of Finance and EconomicsCN1 paper
- Soochow UniversityCN1 paper
- Technische Universität DresdenDE1 paper
- Tencent (China)CN1 paper
- Tsinghua UniversityCN1 paper
- University of AucklandNZ1 paper
9 papers
Mention Attention for Pronoun Translation
Gongbo Tang, Christian Hardmeier
Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with th…
Rhythm-controllable Attention with High Robustness for Long Sentence Speech Synthesis
Dengfeng Ke, Yayue Deng, Yukang Jia +6
Regressive Text-to-Speech (TTS) system utilizes attention mechanism to generate alignment between text and acoustic feature sequence. Alignment determines synthesis robustness (e.g…
An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures
Dengfeng Ke, Yuxing Lu, Xudong Liu +3
With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digi…
Formant Tracking Using Dilated Convolutional Networks Through Dense Connection with Gating Mechanism
Wang Dai, Jinsong Zhang, Yingming Gao +4
Formant tracking is one of the most fundamental problems in speech processing. Traditionally, formants are estimated using signal processing methods. Recent studies showed that gen…
Incorporating Sememes into Chinese Definition Modeling
Liner Yang, Cunliang Kong, Yun Chen +3
Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we construct the Chinese Defi…
Convolution Forgetting Curve Model for Repeated Learning
Yanlu Xie, Yue Chen, Man Li
Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of for…