126 citations · 140 across the 13 of their papers we have counts for
13 papers
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement
Rui-Chen Zheng, Yang Ai, Zhen-Hua Ling
Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-o…
Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement
Ye-Xin Lu, Yang Ai, Zhen-Hua Ling
Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase es…
MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra
Ye-Xin Lu, Yang Ai, Zhen-Hua Ling
This paper proposes MP-SENet, a novel Speech Enhancement Network which directly denoises Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture…
BASEN: Time-Domain Brain-Assisted Speech Enhancement Network with Convolutional Cross Attention in Multi-talker Conditions
Jie Zhang, Qing-Tian Xu, Qiu-Shi Zhu +1
Time-domain single-channel speech enhancement (SE) still remains challenging to extract the target speaker without any prior information on multi-talker conditions. It has been sho…
APNet: An All-Frame-Level Neural Vocoder Incorporating Direct Prediction of Amplitude and Phase Spectra
Yang Ai, Zhen-Hua Ling
This paper presents a novel neural vocoder named APNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra directly. The APNet voco…
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation
Rui-Chen Zheng, Yang Ai, Zhen-Hua Ling
Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-o…
Source-Filter-Based Generative Adversarial Neural Vocoder for High Fidelity Speech Synthesis
Ye-Xin Lu, Yang Ai, Zhen-Hua Ling
This paper proposes a source-filter-based generative adversarial neural vocoder named SF-GAN, which achieves high-fidelity waveform generation from input acoustic features by intro…
Speech Reconstruction from Silent Tongue and Lip Articulation By Pseudo Target Generation and Domain Adversarial Training
Rui-Chen Zheng, Yang Ai, Zhen-Hua Ling
This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra…
Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation
Jing-Xuan Zhang, Genshun Wan, Zhen-Hua Ling +3
In this work, we present a novel method, named AV2vec, for learning audio-visual speech representations by multimodal self-distillation. AV2vec has a student and a teacher module,…
Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations
Chang Liu, Zhen-Hua Ling, Ling-Hui Chen
This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid relianc…
Speaker Adaption with Intuitive Prosodic Features for Statistical Parametric Speech Synthesis
Pengyu Cheng, Zhenhua Ling
In this paper, we propose a method of speaker adaption with intuitive prosodic features for statistical parametric speech synthesis. The intuitive prosodic features employed in thi…
Cognitive Diagnosis with Explicit Student Vector Estimation and Unsupervised Question Matrix Learning
Lu Dong, Zhenhua Ling, Qiang Ling +1
Cognitive diagnosis is an essential task in many educational applications. Many solutions have been designed in the literature. The deterministic input, noisy "and" gate (DINA) mod…
Using multiple reference audios and style embedding constraints for speech synthesis
Cheng Gong, Longbiao Wang, Zhenhua Ling +2
The end-to-end speech synthesis model can directly take an utterance as reference audio, and generate speech from the text with prosody and speaker characteristics similar to the r…