1.2k citations · 2.5k across the 57 of their papers we have counts for
9 papers · 1 filter
Long-frame-shift Neural Speech Phase Prediction with Spectral Continuity Enhancement and Interpolation Error Compensation
Yang Ai, Ye-Xin Lu, Zhen-Hua Ling
Speech phase prediction, which is a significant research focus in the field of signal processing, aims to recover speech phase spectra from amplitude-related features. However, exi…
Neural Speech Phase Prediction based on Parallel Estimation Architecture and Anti-Wrapping Losses
Yang Ai, Zhen-Hua Ling
This paper presents a novel speech phase prediction model which predicts wrapped phase spectra directly from amplitude spectra by neural networks. The proposed model is a cascade o…
Denoising-and-Dereverberation Hierarchical Neural Vocoder for Robust Waveform Generation
Yang Ai, Haoyu Li, Xin Wang +2
This paper presents a denoising and dereverberation hierarchical neural vocoder (DNR-HiNet) to convert noisy and reverberant acoustic features into a clean speech waveform. We impl…
Reverberation Modeling for Source-Filter-based Neural Vocoder
Yang Ai, Xin Wang, Junichi Yamagishi +1
This paper presents a reverberation module for source-filter-based neural vocoders that improves the performance of reverberant effect modeling. This module uses the output wavefor…
Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling
Yuan-Hao Yi, Yang Ai, Zhen-Hua Ling +1
This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS). Singing voice differs from speech and it contains m…
A Neural Vocoder with Hierarchical Generation of Amplitude and Phase Spectra for Statistical Parametric Speech Synthesis
Yang Ai, Zhen-Hua Ling
This paper presents a neural vocoder named HiNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra hierarchically. Different from…
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…
Waveform Modeling and Generation Using Hierarchical Recurrent Neural Networks for Speech Bandwidth Extension
Zhen-Hua Ling, Yang Ai, Yu Gu +1
This paper presents a waveform modeling and generation method using hierarchical recurrent neural networks (HRNN) for speech bandwidth extension (BWE). Different from conventional…