105 citations · 155 across the 6 of their papers we have counts for
6 papers
Voice Attribute Editing with Text Prompt
Zhengyan Sheng, Yang Ai, Li-Juan Liu +2
Despite recent advancements in speech generation with text prompt providing control over speech style, voice attributes in synthesized speech remain elusive and challenging to cont…
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer
Jing-Xuan Zhang, Li-Juan Liu, Yan-Nian Chen +4
With the development of automatic speech recognition (ASR) and text-to-speech synthesis (TTS) technique, it's intuitive to construct a voice conversion system by cascading an ASR a…
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Xin Wang, Junichi Yamagishi, Massimiliano Todisco +37
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is v…
End-to-End Emotional Speech Synthesis Using Style Tokens and Semi-Supervised Training
Peng-fei Wu, Zhen-hua Ling, Li-juan Liu +3
This paper proposes an end-to-end emotional speech synthesis (ESS) method which adopts global style tokens (GSTs) for semi-supervised training. This model is built based on the GST…
Improving Sequence-to-Sequence Acoustic Modeling by Adding Text-Supervision
Jing-Xuan Zhang, Zhen-Hua Ling, Yuan Jiang +3
This paper presents methods of making using of text supervision to improve the performance of sequence-to-sequence (seq2seq) voice conversion. Compared with conventional frame-to-f…
Sequence-to-Sequence Acoustic Modeling for Voice Conversion
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Juan Liu +2
In this paper, a neural network named Sequence-to-sequence ConvErsion NeTwork (SCENT) is presented for acoustic modeling in voice conversion. At training stage, a SCENT model is es…