activity
20192023
most citedMulti-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis

42 citations · 42 across the 6 of their papers we have counts for

collaborators

6 papers

eess.AS2023

AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data

Jianwei Yu, Hangting Chen, Yanyao Bian +6

Recently, the utilization of extensive open-sourced text data has significantly advanced the performance of text-based large language models (LLMs). However, the use of in-the-wild…

eess.AS2023

SnakeGAN: A Universal Vocoder Leveraging DDSP Prior Knowledge and Periodic Inductive Bias

Sipan Li, Songxiang Liu, Luwen Zhang +5

Generative adversarial network (GAN)-based neural vocoders have been widely used in audio synthesis tasks due to their high generation quality, efficient inference, and small compu…

cs.SD2022

Automatic Prosody Annotation with Pre-Trained Text-Speech Model

Ziqian Dai, Jianwei Yu, Yan Wang +5

Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability. However, the acquisition of prosodic boundary labels relies on…

cs.SD2022

Content-Dependent Fine-Grained Speaker Embedding for Zero-Shot Speaker Adaptation in Text-to-Speech Synthesis

Yixuan Zhou, Changhe Song, Xiang Li +5

Zero-shot speaker adaptation aims to clone an unseen speaker's voice without any adaptation time and parameters. Previous researches usually use a speaker encoder to extract a glob…

cs.CL2021

Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis

Yixuan Zhou, Changhe Song, Jingbei Li +4

Exploiting rich linguistic information in raw text is crucial for expressive text-to-speech (TTS). As large scale pre-trained text representation develops, bidirectional encoder re…

cs.CL2019★ 42 cited

Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis

Yanyao Bian, Changbin Chen, Yongguo Kang +1

Speech style control and transfer techniques aim to enrich the diversity and expressiveness of synthesized speech. Existing approaches model all speech styles into one representati…