<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper explores the process of traditional voiceprint recognition, analyzes the traditional GMM recognition algorithm, and proposes a GE2E-based voiceprint recognition algorithm by combining it with the deep neural network. It firstly uses the Bi-GRU network to replace the LSTM network to prevent the lack of semantic information, then adds the SGD algorithm to optimize the speech features, and finally improves the stability and accuracy of recognition by the GE2E loss function. On this basis, a voiceprint recognition system based on GE2E is designed, and the overall performance of the system is tested. Additionally, a voiceprint recognition system is being explored for fault localization. The results show that the recognition accuracy of male voiceprints in the test is at [0.89,0.95], and the recognition accuracy of female voiceprints is at [0.88,0.96], and there is not much difference in the voiceprint recognition accuracy of the voiceprint recognition system for both male and female students, and the overall recognition accuracy is greater than 0.9. When applied in fault location, the error between the measured distance and the actual fault distance is within 0.1 meters, enabling fault location.</p>
</abstract>

This paper explores the process of traditional voiceprint recognition, analyzes the traditional GMM recognition algorithm, and proposes a GE2E-based voiceprint recognition algorithm by combining it with the deep neural network. It firstly uses the Bi-GRU network to replace the LSTM network to prevent the lack of semantic information, then adds the SGD algorithm to optimize the speech features, and finally improves the stability and accuracy of recognition by the GE2E loss function. On this basis, a voiceprint recognition system based on GE2E is designed, and the overall performance of the system is tested. Additionally, a voiceprint recognition system is being explored for fault localization. The results show that the recognition accuracy of male voiceprints in the test is at [0.89,0.95], and the recognition accuracy of female voiceprints is at [0.88,0.96], and there is not much difference in the voiceprint recognition accuracy of the voiceprint recognition system for both male and female students, and the overall recognition accuracy is greater than 0.9. When applied in fault location, the error between the measured distance and the actual fault distance is within 0.1 meters, enabling fault location.

Deep neural network based acoustic pattern recognition system for fault localization application

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper explores the process of traditional voiceprint recognition, analyzes the traditional GMM recognition algorithm, and proposes a GE2E-based voiceprint...

Deep neural network based acoustic pattern recognition system for fault localization application

Data publikacji: 25 lis 2023

Zakres stron: -

Otrzymano: 26 lut 2023

Przyjęty: 05 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01232

Słowa kluczowe
Voiceprint recognition, GMM recognition algorithm, Deep neural network, GE2E recognition algorithm, Fault localization

© 2023 Yao Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep neural network based acoustic pattern recognition system for fault localization application

Data publikacji: 25 lis 2023

Zakres stron: -

Otrzymano: 26 lut 2023

Przyjęty: 05 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01232

Słowa kluczoweVoiceprint recognition, GMM recognition algorithm, Deep neural network, GE2E recognition algorithm, Fault localization

© 2023 Yao Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Voiceprint recognition, GMM recognition algorithm, Deep neural network, GE2E recognition algorithm, Fault localization