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Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions

Abstract

With the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are not suitable models to run on smart home devices. In this paper, we compare relatively small Convolutional Neural Networks (CNN) and evaluate effectiveness of speaker recognition using these models on edge devices. In addition, we also apply transfer learning technique to deal with a problem of limited training data. By developing solution suitable for running inference locally on edge devices, we eliminate the well-known cloud computing issues, such as data privacy and network latency, etc. The preliminary results proved that the chosen model adapts the benefit of computer vision task by using CNN and spectrograms to perform speaker classification with precision and recall ~84 % in time less than 60 ms on mobile device with Atom Cherry Trail processor.

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Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Title of issue:
2018 11th International Conference on Human System Interaction (HSI) strony 139 - 145
Language:
English
Publication year:
2018
Bibliographic description:
Wang M., Sirlapu T., Kwaśniewska A., SZANKIN M., Bartscherer M., Nicolas R.: Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions// 2018 11th International Conference on Human System Interaction (HSI)/ : , 2018, s.139-145
DOI:
Digital Object Identifier (open in new tab) 10.1109/hsi.2018.8431363
Verified by:
Gdańsk University of Technology

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