文章基本信息

标题：Feature Extraction Analysis for Hidden Markov Models in Sundanese Speech Recognition
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作者：Intan Nurma Yulita ; Akik Hidayat ; Atje Setiawan Abdullah 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2018
卷号：16
期号：5
页码：2191-2198
DOI：10.12928/telkomnika.v16i5.7927
语种：English
出版社：Universitas Ahmad Dahlan
其他摘要：Sundanese language is one of the popular languages in Indonesia. Thus, research in Sundanese language becomes essential to be made. It is the reason this study was being made. The vital parts to get the high accuracy of recognition are feature extraction and classifier. The important goal of this study was to analyze the first one. Three types of feature extraction tested were Linear Predictive Coding (LPC), Mel Frequency Cepstral Coefficients (MFCC), and Human Factor Cepstral Coefficients (HFCC). The results of the three feature extraction became the input of the classifier. The study applied Hidden Markov Models as its classifier. However, before the classification was done, we need to do the quantization. In this study, it was based on clustering. Each result was compared against the number of clusters and hidden states used. The dataset came from four people who spoke digits from zero to nine as much as 60 times to do this experiments. Finally, it showed that all feature extraction produced the same performance for the corpus used.
关键词：linear predictive coding (LPC); mel frequency cepstral coefficients (MFCC); human factor cepstral coefficients (HFCC); hidden markov models; speech recognition