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- W4308982542 abstract "At each integrated work stage, many technologies and algebraic formulations are required to identify the speakers from speech signals. Due to advancements in machine learning and artificial intelligence, speaker recognition technologies are gaining popularity and are being used in a variety of applications. This paper presents a multi-feature adaptive boosted hybrid random forest support vector machine (AB-RFSVM) model that can process real-time, uninterrupted speech with 98 percent accuracy. To build the speaker recognition model, the extracted temporal and spectral speaker-specific information was applied as a feature vector to a suggested hybrid machine learning classifier. The goal of this work is to enhance the RF-SVM classifier’s categorization performance by adaptively boosting the decision of RFSVM. SVM moves around the decision trees positioned in the experimentally discovered subareas around the hyperplane dividing the multi-classes of correctly and incorrectly identified speakers is subject to boosting of classification decisions collected using the RFSVM classifier. The predetermined default configuration value was utilized while building the RFSVM classifier. Finally, the efficacy of the AB-RFSVM method is evaluated against that of other baseline methods using a variety of typical and real-time speaker databases. The experimental results revealed that AB-RFSVM yielded a 99 percent specificity and a loss function of less than 2%. This paper’s findings show that the hybrid AB-RFSVM classification method is more effective for identifying and verifying speaker data." @default.
- W4308982542 created "2022-11-20" @default.
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- W4308982542 date "2022-12-01" @default.
- W4308982542 modified "2023-10-01" @default.
- W4308982542 title "Adaptive boosted random forest-support vector machine based classification scheme for speaker identification" @default.
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- W4308982542 doi "https://doi.org/10.1016/j.asoc.2022.109826" @default.
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