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Combining multi-task autoencoder with Wasserstein generative adversarial networks for improving speech recognition performance

Authors
Kao, Chao YuanKo, Hanseok
Issue Date
11월-2019
Publisher
ACOUSTICAL SOC KOREA
Keywords
Speech enhancement; Wasserstein Generative Adversarial Network (WGAN); Weight initialization; Robust speech recognition; Deep Neural Network (DNN)
Citation
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, v.38, no.6, pp.670 - 677
Indexed
SCOPUS
KCI
Journal Title
JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA
Volume
38
Number
6
Start Page
670
End Page
677
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/62044
DOI
10.7776/ASK.2019.38.6.670
ISSN
1225-4428
Abstract
As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and Multi-Task AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.
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공과대학 (전기전자공학부)
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