Speaker Recognition for Robotic Control via an IoT Device

Zhanibek Kozhirbayev, Berat A. Erol, Altynbek Sharipbay, Mo Jamshidi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Speaker Recognition is considered as one of the primary tasks in speech processing. Nowadays, the speaker identification method has been extensively appealing for its broad application in many fields, such as smart environments, securing the cyber-physical systems, speech communications, and robotic controls. Researchers are targeting to perform an effective method that makes it possible to obtain the recognition ability that is close to the hearing of human. In order to get high accuracy, challenges of large-scale applications of speaker identification are overcome through applying techniques not only traditional models based on the GMM, but also deep learning methods. Aiming at effectively dealing with this challenge, in this paper, we present a novel model to increase the recognition accuracy of the short utterance speaker recognition system. We developed a technique to train a Neural Network (NN) on the extracted Mel-Frequency Cepstral Coefficient (MFCC) features from audio samples. Therefore, the recognition system gains the significant accuracy. The model was trained using open-source high-level neural networks API Keras.

Original languageEnglish
Title of host publication2018 World Automation Congress, WAC 2018
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9781532377914
Publication statusPublished - Aug 8 2018
Event2018 World Automation Congress, WAC 2018 - Stevenson, United States
Duration: Jun 3 2018Jun 6 2018


Conference2018 World Automation Congress, WAC 2018
CountryUnited States


  • Amazon Echo
  • Human robot interactions
  • Internet of robotic things
  • Neural networks
  • Speaker identification and recognition

ASJC Scopus subject areas

  • Control and Systems Engineering

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