Multi-Accent Speaker Detection Using Normalize Feature MFCC Neural Network Method

Multi-Accent Speaker Detection Using Normalize Feature MFCC Neural Network Method

speaker recognition; classification; multi accent; MFCC; neural network.

Speaker recognition is a field of research that continues to this day. Various methods have been developed to detect the human
voice with greater precision and accuracy. Research on human speech recognition that is quite challenging is accent
recognition. Detecting various types of human accents with different accents and ethnicities with high accuracy is a research
that is quite difficult to do. According to the results of the research on the data preprocessing stage, feature extraction and the
selection of the right classification method play a very important role in determining the accuracy results. This study uses a
preprocessing approach with normalizing features combined with MFCC as a method for performing feature extraction and
Neural Network (NN) which is a classification method that works based on the workings of the human brain. Research results
obtained using the normalize feature with MFCC and Neural Network for multi-accent speaker recognition, the accuracy
performance reaches 82.68%, precision is 83% and recall is 82.88%.

Kristiawan Nugroho, Edy Winarno, Eri Zuliarso, Sunardi

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

August 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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