Voice Verification System Based on Bark-frequency Cepstral Coefficient

Authors

  • Karisma Trinanda Putra Universitas Muhammadiyah Yogyakarta

DOI:

https://doi.org/10.18196/jet.1104

Keywords:

artificial neural network, bark-frequency cepstral coefficient, voice activity detection

Abstract

Data verification systems evolve towards a more natural system using biometric media. In daily interactions, human use voice as a tool to communicate with others. Voice charactheristic is also used as a tool to identify subjects who are speaking. The problem is that background noise and signal characteristics of each person which is unique, cause speaker classification process becomes more complex. To identify the speaker, we need to understand the speech signal feature extraction process. We developed the technology to extract voice characteristics of each speaker based on spectral analysis. This research is useful for the development of biometric-based security application. At first, the voice signal will be separated by a pause signal using voice activity detection. Then the voice characteristic will be extracted using a bark-frequency cepstral coefficient. Set of cepstral will be classified according to the speaker, using artificial neural network. The accuracy reached about 82% in voice recognition process with 10 speakers, meanwhile, the highest accuracy was 93% with only 1 speaker. 

References

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Published

2017-03-31

How to Cite

Putra, K. T. (2017). Voice Verification System Based on Bark-frequency Cepstral Coefficient. Journal of Electrical Technology UMY, 1(1), 28–36. https://doi.org/10.18196/jet.1104

Issue

Vol. 1 No. 1 (2017)

Section

Articles

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