3 citations · 11 across the 9 of their papers we have counts for
9 papers
Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Prior
Haoyu Li, Xuan Wang, Tong Liu +2
The anti-interference capability of wireless links is a physical layer problem for edge computing. Although convolutional codes have inherent error correction potential due to the…
Joint Noise Reduction and Listening Enhancement for Full-End Speech Enhancement
Haoyu Li, Yun Liu, Junichi Yamagishi
Speech enhancement (SE) methods mainly focus on recovering clean speech from noisy input. In real-world speech communication, however, noises often exist in not only speaker but al…
DDS: A new device-degraded speech dataset for speech enhancement
Haoyu Li, Junichi Yamagishi
A large and growing amount of speech content in real-life scenarios is being recorded on consumer-grade devices in uncontrolled environments, resulting in degraded speech quality.…
Multi-Metric Optimization using Generative Adversarial Networks for Near-End Speech Intelligibility Enhancement
Haoyu Li, Junichi Yamagishi
The intelligibility of speech severely degrades in the presence of environmental noise and reverberation. In this paper, we propose a novel deep learning based system for modifying…
Enhancing Low-Quality Voice Recordings Using Disentangled Channel Factor and Neural Waveform Model
Haoyu Li, Yang Ai, Junichi Yamagishi
High-quality speech corpora are essential foundations for most speech applications. However, such speech data are expensive and limited since they are collected in professional rec…
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…
Improved Prosody from Learned F0 Codebook Representations for VQ-VAE Speech Waveform Reconstruction
Yi Zhao, Haoyu Li, Cheng-I Lai +3
Vector Quantized Variational AutoEncoders (VQ-VAE) are a powerful representation learning framework that can discover discrete groups of features from a speech signal without super…
Noise Tokens: Learning Neural Noise Templates for Environment-Aware Speech Enhancement
Haoyu Li, Junichi Yamagishi
In recent years, speech enhancement (SE) has achieved impressive progress with the success of deep neural networks (DNNs). However, the DNN approach usually fails to generalize wel…
iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric Learning
Haoyu Li, Szu-Wei Fu, Yu Tsao +1
The intelligibility of natural speech is seriously degraded when exposed to adverse noisy environments. In this work, we propose a deep learning-based speech modification method to…