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Voice Activation Systems for Embedded Devices: Systematic Literature Review
Volume 31, Issue 1 (2020), pp. 65–88
Pub. online: 23 March 2020      Type: Research Article      Open accessOpen Access
Received
1 January 2019
Accepted
1 November 2019
Published
23 March 2020

Abstract

A large number of modern mobile devices, embedded devices and smart home devices are equipped with a voice control. Automatic recognition of the entire audio stream, however, is undesirable for the reasons of the resource consumption and privacy. Therefore, most of these devices use a voice activation system, whose task is to find the specified in advance word or phrase in the audio stream (for example, Ok, Google) and to activate the voice request processing system when it is found. The voice activation system must have the following properties: high accuracy, ability to work entirely on the device (without using remote servers), consumption of a small amount of resources (primarily CPU and RAM), noise resistance and variability of speech, as well as a small delay between the pronunciation of the key phrase and the system activation. This work is a systematic literature review on voice activation systems that satisfy the above properties. We describe the principle of various voice activation systems’ operation, the characteristic representation of sound in such systems, consider in detail the acoustic modelling and, finally, describe the approaches used to assess the models’ quality. In addition, we point to a number of open questions in this problem.

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Biographies

Kolesau Aliaksei

A. Kolesau is a PhD student at Department of Information Technologies, Vilnius Gediminas Technical University. His research interests include machine learning and speech recognition.

Šešok Dmitrij
dmitrij.sesok@vgtu.lt

D. Šešok is a professor at Department of Information Technologies, Vilnius Gediminas Technical University. His fields of interest are global optimization and machine learning. He has authored or co-authored around 40 papers.


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voice activation keyword spotter hidden markov models acoustic model neural networks

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