Detection and Recognition of Voice Commands by a Distributed Acoustic Sensor Based on Phase-Sensitive OTDR in the Smart Home Concept-Reference-Cited by-同舟云学术

Detection and Recognition of Voice Commands by a Distributed Acoustic Sensor Based on Phase-Sensitive OTDR in the Smart Home Concept

Published:2024-04-03 Issue:7 Volume:24 Page:2281
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Gritsenko Tatyana V.¹^ORCID,Orlova Maria V.¹,Zhirnov Andrey A.¹^ORCID,Konstantinov Yuri A.²^ORCID,Turov Artem T.²³,Barkov Fedor L.²^ORCID,Khan Roman I.¹,Koshelev Kirill I.¹,Svelto Cesare⁴^ORCID,Pnev Alexey B.¹

Affiliation:

1. Laser and Optoelectronic Systems Department, Radio Electronics and Laser Technology Faculty, Bauman Moscow State Technical University, 2-nd Baumanskaya 5-1, 105005 Moscow, Russia

2. Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences (PFRC UB RAS), 13a Lenina St., 614990 Perm, Russia

3. General Physics Department, Applied Mathematics and Mechanics Faculty, Perm National Research, Polytechnic University, Prospekt Komsomolsky 29, 614990 Perm, Russia

4. Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy

Abstract

In recent years, attention to the realization of a distributed fiber-optic microphone for the detection and recognition of the human voice has increased, whereby the most popular schemes are based on φ-OTDR. Many issues related to the selection of optimal system parameters and the recognition of registered signals, however, are still unresolved. In this research, we conducted theoretical studies of these issues based on the φ-OTDR mathematical model and verified them with experiments. We designed an algorithm for fiber sensor signal processing, applied a testing kit, and designed a method for the quantitative evaluation of our obtained results. We also proposed a new setup model for lab tests of φ-OTDR single coordinate sensors, which allows for the quick variation of their parameters. As a result, it was possible to define requirements for the best quality of speech recognition; estimation using the percentage of recognized words yielded a value of 96.3%, and estimation with Levenshtein distance provided a value of 15.

Funder

State Assignment

Russian Science Foundation

Publisher

MDPI AG

Link

https://www.mdpi.com/1424-8220/24/7/2281/pdf

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