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Sensors and Materials, Volume 37, Number 1(4) (2025)
Copyright(C) MYU K.K.
pp. 441-451
S&M3919 Research Paper of Special Issue
https://doi.org/10.18494/SAM5466
Published: January 31, 2025

Text-independent Hakka Speaker Recognition in Noisy Environments [PDF]

Jie Peng, Chin-Ta Chen, and Cheng-Fu Yang

(Received November 11, 2024; Accepted January 20, 2025)

Keywords: speaker identification, estimate-maximize algorithm, Gaussian mixture model, mel-frequency cepstral coefficient, Hakka

In this study, we introduce a robust text-independent speaker recognition system tailored specifically for Hakka speakers, operating in diverse noisy environments. Our system employs mel-frequency cepstral coefficients for effective feature extraction, coupled with a Gaussian mixture model and the expectation-maximization algorithm to enhance accuracy under challenging conditions. Through comprehensive experimentation, we achieved impressive recognition rates, underscoring the system’s effectiveness for real-world applications. This innovation not only addresses the unique characteristics of Hakka speech but also demonstrates significant potential for deployment in various settings where background noise poses a challenge.

Corresponding author: Jie Peng and Cheng-Fu Yang


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Cite this article
Jie Peng, Chin-Ta Chen, and Cheng-Fu Yang, Text-independent Hakka Speaker Recognition in Noisy Environments, Sens. Mater., Vol. 37, No. 1, 2025, p. 441-451.

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