Paper
6 September 2024 The influence of the number of mel-frequency cepstral coefficients on the voice recognition error probability by a distributed fiber optic sensor
Olga Yu. Gubareva, Vadim M. Kartak, Michael V. Dashkov, Igor S. Makarov, Vladimir O. Gureev, Alexander S. Evtushenko
Author Affiliations +
Proceedings Volume 13168, Optical Technologies for Telecommunications 2023; 131680H (2024) https://doi.org/10.1117/12.3026246
Event: Optical Technologies for Telecommunications 2023, 2023, Kazan, Russian Federation
Abstract
The study explores the potential applications of fiber optic sensors for user verification tasks. An experimental validation of the considered methods for recognizing speech signals obtained from acoustic sensors is conducted. In the experiment, voices of four different speakers were recorded. Each speaker sequentially uttered the same text simultaneously using a capacitor microphone and a fiber optic distributed sensor. To create the dataset, each speaker read three prepared texts. After training the system on the dataset and achieving speech signal recognition with sufficiently high accuracy recorded from the capacitor microphone, attention was turned to addressing the challenge of noise elimination. For this purpose, background noise was separately recorded at the experimental site. The conclusion is drawn regarding the applicability of mel-frequency cepstral coefficients for solving the task of identifying individuals based on the sound signal obtained from the fiber optic sensor with a specified accuracy.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Olga Yu. Gubareva, Vadim M. Kartak, Michael V. Dashkov, Igor S. Makarov, Vladimir O. Gureev, and Alexander S. Evtushenko "The influence of the number of mel-frequency cepstral coefficients on the voice recognition error probability by a distributed fiber optic sensor", Proc. SPIE 13168, Optical Technologies for Telecommunications 2023, 131680H (6 September 2024); https://doi.org/10.1117/12.3026246
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KEYWORDS
Fiber optics sensors

Signal to noise ratio

Biometrics

Data modeling

Speaker recognition

Acoustics

Education and training

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