Optimization of Feature Extraction in Indonesian Speech Recognition Using PCA and SVM Classification

Speech recognition is a technology application that communicates with machines by identifying the speaker's words. The main processes in speech recognition are pre-processing, feature extraction and classification. The feature extraction method is one of the most studied sections in speech recognition research because it plays an important role in obtaining optimal features. MFCC has become a popular feature extraction method widely applied to speech recognition systems. In addition, the IMFCC, GFCC, BFCC, and LFCC methods have also begun to be widely applied. Each method can model some information and, by doing a combination, is considered able to represent more information. The feature extraction combination system generates more feature data, and optimization is done to reduce the feature coefficient. This paper discusses testing machine learning models on each feature extraction method and a combination of feature extraction methods to determine the best level of accuracy. Furthermore, this paper also discusses the effect of using the PCA method, which is applied to a combination of feature extraction methods in reducing the number of features to find out the best number of features. Support vector machine (SVM) is proposed as a classification process in this research. The voice sample consisted of 800 in the form of words spoken in Indonesian, namely the numbers zero to nine (0–9). The results showed that combining feature extraction methods resulted in better accuracy than individual methods. The combined GFCC+LFCC method produces the best accuracy of 99.38% while using independent methods produces the best accuracy of 99.38% using the GFCC method. Applying the PCA method to GFCC+LFCC can reduce feature dimensions to 16 coefficients while maintaining the highest level of accuracy. This paper is expected to be a reference in further speech recognition research.