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

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    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.

     

     


  • Keywords


    FFNN; RF; MFCC; Pitch Period; Sampling Rate; Neurons; Weight.

  • References


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Article ID: 30815
 
DOI: 10.14419/ijet.v9i2.30815




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