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Design and implementation of speech recognition system integrated with internet of things

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 2 (2021)

DOI
10.11591/ijece.v11i2.pp1796-1803
ISSN
2722-2578, 2088-8708
Published
2021-04-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The process of speech recognition is such that a speech signal from a client or user is received by the system through a microphone, then the system analyses this signal and extracts useful information from the signal which is converted to text. This study focuses on the design and implementation of a speech recognition system integrated with internet of thing (IoT) to control electrical appliances and door with raspberry pi as a core element. To design the speech recognition system, digital signal processing (DSP) technique and hidden Markov model were fully considered for processing, extraction and high predictive accuracy of the system. The Google application programming interface (API) was used as a cloud server to store command and give the system to assess to the internet. With 150 speech samples on the system, a high level of accuracy of over 80% was obtained.

Collaboration network of the authors

21 authors · 50 co-authorship links · 12 across different organisations

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  • Covenant University
  • Namibia University of Science and Technology
  • Federal University of Agriculture, Abeokuta
  • Kwara State University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
C. O. A. AwosopeCovenant University2
Adeyinka Ajao AdewaleCovenant University2
Lambe Mutalub AdesinaKwara State University2
A. E. Tope-OjoCovenant University1
Aderibigbe Israel AdekitanCovenant University1
Ayokunle A. AwelewaCovenant University1
Isaac Adekunle SamuelCovenant University1
James KatendeNamibia University of Science and Technology1
Awosope Claudius O. A.Covenant University1
A. ChiagoroCovenant University1
Adedasola A. AdemolaCovenant University1
A. OluwabusolaCovenant University1
Simeon MatthewFederal University of Agriculture, Abeokuta1
Hope Evwieroghene OrovwodeCovenant University1
Abdulkareem AdemolaCovenant University1
Oladipupo Oluwafemi K.Covenant University1
Agbetuyi Oluranti A.Affiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Design and implementation of speech recognition system integrated with internet of things</dc:title><dc:creator>Abdulkareem, Ademola</dc:creator><dc:creator>Somefun, Tobiloba E.</dc:creator><dc:creator>Chinedum, Oji K.</dc:creator><dc:creator>Agbetuyi, Felix</dc:creator><dc:description>&lt;jats:p&gt;The process of speech recognition is such that a speech signal from a client or user is received by the system through a microphone, then the system analyses this signal and extracts useful information from the signal which is converted to text. This study focuses on the design and implementation of a speech recognition system integrated with internet of thing (IoT) to control electrical appliances and door with raspberry pi as a core element. To design the speech recognition system, digital signal processing (DSP) technique and hidden Markov model were fully considered for processing, extraction and high predictive accuracy of the system. The Google application programming interface (API) was used as a cloud server to store command and give the system to assess to the internet. With 150 speech samples on the system, a high level of accuracy of over 80% was obtained.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-04-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v11i2.pp1796-1803</dc:identifier><dc:identifier>10.11591/ijece.v11i2.pp1796-1803</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 2 (2021); 1796</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/23663/14701</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>