77 citations · 243 across the 31 of their papers we have counts for
6 papers · 1 filter
Unsupervised neural adaptation model based on optimal transport for spoken language identification
Xugang Lu, Peng Shen, Yu Tsao +1
Due to the mismatch of statistical distributions of acoustic speech between training and testing sets, the performance of spoken language identification (SLID) could be drastically…
A Study of Incorporating Articulatory Movement Information in Speech Enhancement
Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang +3
Although deep learning algorithms are widely used for improving speech enhancement (SE) performance, the performance remains limited under highly challenging conditions, such as un…
Improving Perceptual Quality by Phone-Fortified Perceptual Loss using Wasserstein Distance for Speech Enhancement
Tsun-An Hsieh, Cheng Yu, Szu-Wei Fu +2
Speech enhancement (SE) aims to improve speech quality and intelligibility, which are both related to a smooth transition in speech segments that may carry linguistic information,…
Incorporating Broad Phonetic Information for Speech Enhancement
Yen-Ju Lu, Chien-Feng Liao, Xugang Lu +2
In noisy conditions, knowing speech contents facilitates listeners to more effectively suppress background noise components and to retrieve pure speech signals. Previous studies ha…
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
Tsun-An Hsieh, Hsin-Min Wang, Xugang Lu +1
Due to the simple design pipeline, end-to-end (E2E) neural models for speech enhancement (SE) have attracted great interest. In order to improve the performance of the E2E model, t…
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
Cheng Yu, Ryandhimas E. Zezario, Syu-Siang Wang +5
Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model gener…