Open AccessVoice pattern recognition using Mel-Frequency Cepstral Coefficient and Hidden Markov Model for bahasa Madura
Author(s) -
Ubaidi Ubaidi,
Nindian Puspa Dewi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1375/1/012057
Subject(s) - mel frequency cepstrum , computer science , speech recognition , hidden markov model , set (abstract data type) , matching (statistics) , signal (programming language) , speaker recognition , artificial intelligence , feature extraction , pattern recognition (psychology) , natural language processing , mathematics , statistics , programming language
Voice recognition is one part of an application that allows a device to recognize spoken words by digitizing words and matching digital signals with a particular pattern stored in a device. Spoken words are converted into digital signals by converting voice waves into a set of numbers which is then compared with the voice pattern to identify the words. MFCC can be an alternative method to solve the problem of voice extraction because this method is reliable for recognizing the unique features of human voice. Hidden Markov Model is used to recognize the voice pattern, so it can be used to compare the voice signal obtained from e-learning with the trained voice signal. Bahasa Madura is a regional language used by ethnic Madurese to communicate daily. Currently the number of Madurese people who understand this language is reduced so that the use of Bahasa Madura is also reduced. Therefore, it is necessary to conduct speech recognition research in Madura Language as one of effort to preserve and develop the use of Regional Language. The experimental results show that the average accuracy for testing the system with one model is 85% and the average accuracy for testing the system with multi model is 90%.