1.2k citations · 2.5k across the 57 of their papers we have counts for
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Decoupled Pronunciation and Prosody Modeling in Meta-Learning-Based Multilingual Speech Synthesis
Yukun Peng, Zhenhua Ling
This paper presents a method of decoupled pronunciation and prosody modeling to improve the performance of meta-learning-based multilingual speech synthesis. The baseline meta-lear…
Predictions of Subjective Ratings and Spoofing Assessments of Voice Conversion Challenge 2020 Submissions
Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang +5
The Voice Conversion Challenge 2020 is the third edition under its flagship that promotes intra-lingual semiparallel and cross-lingual voice conversion (VC). While the primary eval…
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…
Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion
Yi Zhao, Wen-Chin Huang, Xiaohai Tian +5
The voice conversion challenge is a bi-annual scientific event held to compare and understand different voice conversion (VC) systems built on a common dataset. In 2020, we organiz…
Online Speaker Adaptation for WaveNet-based Neural Vocoders
Qiuchen Huang, Yang Ai, Zhenhua Ling
In this paper, we propose an online speaker adaptation method for WaveNet-based neural vocoders in order to improve their performance on speaker-independent waveform generation. In…
Recognition-Synthesis Based Non-Parallel Voice Conversion with Adversarial Learning
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
This paper presents an adversarial learning method for recognition-synthesis based non-parallel voice conversion. A recognizer is used to transform acoustic features into linguisti…
Knowledge-and-Data-Driven Amplitude Spectrum Prediction for Hierarchical Neural Vocoders
Yang Ai, Zhen-Hua Ling
In our previous work, we have proposed a neural vocoder called HiNet which recovers speech waveforms by predicting amplitude and phase spectra hierarchically from input acoustic fe…
An Adaptive X-vector Model for Text-independent Speaker Verification
Bin Gu, Wu Guo, Lirong Dai +1
In this paper, adaptive mechanisms are applied in deep neural network (DNN) training for x-vector-based text-independent speaker verification. First, adaptive convolutional neural…
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…
Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
This paper presents a method of sequence-to-sequence (seq2seq) voice conversion using non-parallel training data. In this method, disentangled linguistic and speaker representation…
Channel adversarial training for cross-channel text-independent speaker recognition
Xin Fang, Liang Zou, Jin Li +2
The conventional speaker recognition frameworks (e.g., the i-vector and CNN-based approach) have been successfully applied to various tasks when the channel of the enrolment datase…
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda +4
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different stat…
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
Tomi Kinnunen, Jaime Lorenzo-Trueba, Junichi Yamagishi +4
Voice conversion (VC) aims at conversion of speaker characteristic without altering content. Due to training data limitations and modeling imperfections, it is difficult to achieve…