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- W3205484782 abstract "Recently attention of researchers towards Automatic Speech Recognition (ASR) is tremendously increased than ever because of its applications in security applications, home applications, military applications, health care applications, language learning applications, and marketing applications. Initially, most of the researches in respect of ASR is confined to robotics and security applications. Further, attention is also motivated towards home applications. Now industrial automation is at the tipping point with ASR. Motivated by this, the proposed paper deals with the Speech Command Recognition (SCR) for next-generation automated vehicles. For SCR, a set of features such as Gammatone Cepstrum Coefficient (GTCC), Mel-Frequency Cepstrum Coefficients (MFCC), and Pitch, Spectral Flux, and Spectral Entropy are considered for training the proposed Support Vector Machine (SVM) classifier. Performance analysis is carried with different training rates and with different noisy conditions. Simulation results depict the superiority of the proposed classifier than the existing methods." @default.
- W3205484782 created "2021-10-25" @default.
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- W3205484782 date "2021-08-03" @default.
- W3205484782 modified "2023-10-16" @default.
- W3205484782 title "Performance Analysis of Speech Command Recognition Using Support Vector Machine Classifiers" @default.
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- W3205484782 doi "https://doi.org/10.1007/978-981-16-1777-5_19" @default.
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