Acoustic data classification using random forest algorithm and feed forward neural network

Speaker identification systems are designed to recognize the speaker or set of speakers according to their acoustic analysis. Many approach-es are made to perform the acoustic analysis in the speech signal, the general description of those systems is time and frequency domain analysis. In this paper...

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Bibliographic Details
Published in:International Journal of Engineering & Technology
Main Authors: Najdet Nasret Coran, Ali, Hayri Sever, Prof Dr., Murad Ahmed Mohammed Amin, Dr.
Format: Article in Journal/Newspaper
Language:English
Published: Science Publishing Corporation 2020
Subjects:
FFNN
RF
MFCC
Pitch Period
Sampling Rate
Neurons
Weight
Arctic
Online Access:https://www.sciencepubco.com/index.php/ijet/article/view/30815
https://doi.org/10.14419/ijet.v9i2.30815
Description
Summary:Speaker identification systems are designed to recognize the speaker or set of speakers according to their acoustic analysis. Many approach-es are made to perform the acoustic analysis in the speech signal, the general description of those systems is time and frequency domain analysis. In this paper, acoustic information is extracted from the speech signals using MFCC and Fundamental Frequency methods combi-nation. The results are classified using two different algorithms such as Random-forest and Feed Forward Neural Network. The FFNN classifier integration with the acoustic model resulted a recognition accuracy of 91.4 %. The CMU ARCTIC Database is referred in this work.

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