Guest Editorial: Advances in Deep Learning for Speech Processing

Guest Editorial: Advances in Deep Learning for Speech Processing Journal of Signal Processing Systems (2018) 90:959–961 https://doi.org/10.1007/s11265-018-1333-3 1 2 3 Lei Xie & Tan Lee & Man-Wai Mak Published online: 17 February 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Deep learning has been pushing the frontiers of various tasks proposed SD approach is robust to the interference of a com- in speech processing, including speech recognition, speech peting speaker even under low target-to-masker ratio (TMR) synthesis, and speaker recognition. This special issue intro- conditions. duces the latest advances in deep learning approaches to spo- While DNN-HMM has become a mainstream method for ken language processing. The papers are the extension of speech recognition, the performance of ASR systems under some from the 10th International Symposium on Chinese adverse acoustic environments is still far from being satisfac- Spoken Language Processing (ISCSLP2016). We briefly tory. In BAuxiliary Features from Laser-Doppler Vibrometer summarize these papers in three areas: speech recognition, Sensor for Deep Neural Network Based Robust Speech speech synthesis and speech meta-data analysis. Recognition^ (https://doi.org/10.1007/s11265-017-1287-x), Sun et al. proposed using a laser-doppler vibrometer (LDV) sensor augmented with a conventional microphones for robust 1 Speech Recognition speech recognition. Unlike the conventional approach where the LDV signals were used http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Signal Processing Systems Springer Journals

Guest Editorial: Advances in Deep Learning for Speech Processing

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics
ISSN
1939-8018
eISSN
1939-8115
D.O.I.
10.1007/s11265-018-1333-3
Publisher site
See Article on Publisher Site

Abstract

Journal of Signal Processing Systems (2018) 90:959–961 https://doi.org/10.1007/s11265-018-1333-3 1 2 3 Lei Xie & Tan Lee & Man-Wai Mak Published online: 17 February 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 Deep learning has been pushing the frontiers of various tasks proposed SD approach is robust to the interference of a com- in speech processing, including speech recognition, speech peting speaker even under low target-to-masker ratio (TMR) synthesis, and speaker recognition. This special issue intro- conditions. duces the latest advances in deep learning approaches to spo- While DNN-HMM has become a mainstream method for ken language processing. The papers are the extension of speech recognition, the performance of ASR systems under some from the 10th International Symposium on Chinese adverse acoustic environments is still far from being satisfac- Spoken Language Processing (ISCSLP2016). We briefly tory. In BAuxiliary Features from Laser-Doppler Vibrometer summarize these papers in three areas: speech recognition, Sensor for Deep Neural Network Based Robust Speech speech synthesis and speech meta-data analysis. Recognition^ (https://doi.org/10.1007/s11265-017-1287-x), Sun et al. proposed using a laser-doppler vibrometer (LDV) sensor augmented with a conventional microphones for robust 1 Speech Recognition speech recognition. Unlike the conventional approach where the LDV signals were used

Journal

Journal of Signal Processing SystemsSpringer Journals

Published: Feb 17, 2018

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