Speaker and Speech Recognition Using Hierarchy Support Vector Machine and Backpropagation

Asti F. Fadlilah, Esmeralda C. Djamal


Voice signal processing has been proposed to improve effectiveness and facilitate the public, such as Smart Home. This study aims a smart home simulation model to move doors, TVs, and lights from voice instructions. Sound signals are processed using Mel-frequency Cepstrum Coefficients (MFCC) to perform feature extraction. Then, the voice is recognized by the speaker using a hierarchy Support Vector Machine (SVM). So that unregistered speakers are not processed or are declared not having access rights. For the process of recognizing spoken words such as "Open the Door”,"Close the Door","Turn on the TV","Turn off the TV","Turn on the Lights" and "Turn Off
the Lights" are done using Backpropagation. The results showed that hierarchy SVM provided an accuracy of 71% compared to the single SVM of 45%.


spoken word recognition; speaker recognition; MFCC; backpropagation; support vector machine

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