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Special Issue "VOICE Sensors with Deep Learning"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 30 December 2020.

Special Issue Editor

Prof. Wookey Lee
Website
Guest Editor
Professor & Director of VOICE AI research institute, Inha University, Incheon, Korea
Interests: VOICE sensor; Deep Learning; Patent, Information Retrieval
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Deep Learning triggering sensor technologies, especially as relates to the VOICE issue.

A person goes through a speaking and listening process that repeats verbal, physiological, and acoustic steps for communication. Voice technology utilizing a range of sensor technologies using biosignals that are measurable in these human voice activities has developed rapidly in recent years. In particular, with the development of voice recognition technology based on artificial intelligence and deep learning, the related market has expanded and is being released into various services. Many sensor issues need to be exploited.

Prof. Wookey Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Voice technology
  • Deep Learning
  • Voice Sensors
  • Biosignal
  • Voice Recognition
  • Voice Generation

Published Papers (1 paper)

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Research

Open AccessArticle
Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data
Sensors 2020, 20(8), 2326; https://doi.org/10.3390/s20082326 - 19 Apr 2020
Abstract
The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new [...] Read more.
The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark. Full article
(This article belongs to the Special Issue VOICE Sensors with Deep Learning)
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