Ensemble of jointly trained deep neural network-based acoustic models for reverberant speech recognition-Reference-Cited by-同舟云学术

Ensemble of jointly trained deep neural network-based acoustic models for reverberant speech recognition

Published:2019-02 Issue: Volume:85 Page:1-9
ISSN:1051-2004
Container-title:Digital Signal Processing
language:en
Short-container-title:Digital Signal Processing

Author:

Lee Moa,Lee Jeehye,Chang Joon-Hyuk^ORCID

Funder

MOTIE

Defense Acquisition Program Administration

Agency for Defense Development

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Signal Processing,Artificial Intelligence,Applied Mathematics,Computer Vision and Pattern Recognition,Statistics, Probability and Uncertainty,Computational Theory and Mathematics

Reference47 articles.

1. Packet loss concealment based on deep neural networks for digital speech transmission;Lee;IEEE/ACM Trans. Audio Speech Lang. Process.,2016

2. EVAM: an eigenvector-based algorithm for multichannel blind deconvolution of input colored signals;Gurelli;IEEE Trans. Signal Process.,1995

3. A two-stage algorithm for one-microphone reverberant speech enhancement;Wu;IEEE Trans. Audio Speech Lang. Process.,2006

4. Suppression of late reverberation effect on speech signal using long-term multiple-step linear prediction;Kinoshita;IEEE Trans. Audio Speech Lang. Process.,2009

5. Speech Dereverberation;Naylor,2010

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-driven hybrid modelling of waves at mid-frequencies range: Application to forward and inverse Helmholtz problems;Journal of Computational Science;2024-09

2. Enhancing medical image classification through controlled diversity in ensemble learning;Engineering Applications of Artificial Intelligence;2024-07

3. Discriminative feature selection using signed Laplacian restricted Boltzmann machine for speed and generalization improvement of high dimensional data classification;Applied Soft Computing;2024-03

4. Self-organizing speech recognition that processes acoustic and articulatory features;Multimedia Tools and Applications;2023-10-07

5. Feature Recognition Method of Digital Piano Audio Signal Based on CNN;2023 International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII);2023-06