De-reverberation using DNN for Non-Reference Reverberant Speech Intelligibility Estimation

被引：0

作者：

Nakazawa, Kazushi ^{[1
]}

Kondo, Kazuhiro ^{[1
]}

机构：

[1] Yamagata Univ, Grad Sch Sci & Engn, Yamagata, Japan

来源：

2018 IEEE 7TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS (GCCE 2018) | 2018年

关键词：

Deep neural network (DNN); supervised learning; dereverberation; speech intelligibility;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Reverberation distorts speech leading to degradation of speech intelligibility and speech quality. We have been investigating on methods to predict the speech intelligibility of reverberant speech. This normally requires clean speech, which is often not available. In this paper we attempt to use Deep neural networks (DNNs) to reconstruct clean speech from distorted speech. This approach proved to be effective for dereverberation in various reverberant environments.

引用

页码：349 / 350

页数：2

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