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Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Recognition System for Cantonese Speakers in Different Noisy Environments Based on Estimate–Maximize Algorithm

Yu Fan, Chin-Ta Chen, and Cheng-Fu Yang

(Received March 29, 2022; Accepted May 24, 2022)

Keywords: speaker identification, estimate–maximize algorithm, Gaussian mixture model, Mel-frequency cepstrum coefficient, maximum likelihood estimation

Highly accurate personal identification systems are required in many different recognition situations. In this study, the Mel-frequency cepstral coefficients was used to extract the features of speakers. The aim of this study was to identify different speeches in different noisy environments. A maximum likelihood estimation method based on noise probability was proposed to enhance the recognition effects of the Gaussian mixture model of different speeches from the mixed noise speech signals. Experimental results indicated that the method had high recognition results under various noise conditions. The recognition results of the proposed method in different noise environments were superior to those of a method using only one type of noise for modeling. Experimental results obtained from some unspecified speakers showed that three different languages (Mandarin, English, and Cantonese) were effectively identified.

Corresponding author: Yu Fan, Cheng-Fu Yang




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