77 citations · 243 across the 31 of their papers we have counts for
6 papers · 1 filter
Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport
Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu +1
This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essenti…
CS-Rep: Making Speaker Verification Networks Embracing Re-parameterization
Ruiteng Zhang, Jianguo Wei, Wenhuan Lu +4
Automatic speaker verification (ASV) systems, which determine whether two speeches are from the same speaker, mainly focus on verification accuracy while ignoring inference speed.…
Siamese Neural Network with Joint Bayesian Model Structure for Speaker Verification
Xugang Lu, Peng Shen, Yu Tsao +1
Generative probability models are widely used for speaker verification (SV). However, the generative models are lack of discriminative feature selection ability. As a hypothesis te…
MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
Szu-Wei Fu, Cheng Yu, Tsun-An Hsieh +4
The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory. Ob…
EMA2S: An End-to-End Multimodal Articulatory-to-Speech System
Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang +4
Synthesized speech from articulatory movements can have real-world use for patients with vocal cord disorders, situations requiring silent speech, or in high-noise environments. In…
Coupling a generative model with a discriminative learning framework for speaker verification
Xugang Lu, Peng Shen, Yu Tsao +1
The speaker verification (SV) task is to decide whether an utterance is spoken by a target or an imposter speaker. For most studies, a log-likelihood ratio (LLR) score is estimated…