Date of Award
December 2021
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering
Committee Member
Melissa Smith
Committee Member
Feng Luo
Committee Member
YingJie Lao
Abstract
Speech enhancement is a critical part in automatic speech recognition systems. Recently with the development of deep learning based techniques, those speech enhancement systems trained with neural networks can significantly improve performance. While many of the latest speech enhancement systems show advantages in maximizing the perceptual quality of the noisy signals, they expose drawbacks when the test noisy signals have noise types that never exist during the system training process. The systems have relatively poor performance when handling noisy signals with unseen noise in contrast to noisy signals with seen noise. The dissimilarity between the training and testing circumstances can cause a serious performance decline in a deep learning task.In this work, a new method is proposed to solve the noise types problem. The framework has three parts: the autoencoder, the gradient reverse layers and the recurrent neural networks. The proposed framework can weaken the noise types influences when handling random noisy signals. This work shows that the new method outperforms the baseline models in unseen noise situations.
Recommended Citation
Lan, Xiang, "Noise Types Adaptation for Speech Enhancement with Recurrent Neural Network" (2021). All Theses. 3638.
https://tigerprints.clemson.edu/all_theses/3638