Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

Abstract

Studies are constantly developing and improving speech recognition systems, especially their accuracy. This study developed an isolated word recognition system by using the syllable number characteristics of speech signals that will be recognized. First, the syllable number of speech signals to be recognized was detected, and then, the detection results were used to call one of the database groups that matched the syllable number characteristics. This method was designed to reduce the error possibility through a matching process between test data features and database features. This study used Mel frequency cepstral coefficients (MFCC) for feature extraction and the K-nearest neighbor (KNN) method for classification. Three versions of the proposed method were designed. The results showed that version three increased the accuracy by 4% compared to the conventional recognition system. Version three had the fastest computational time compared to the other methods. The addition of syllable detection algorithms in version three increased the computational time by only 0.151 s compared to the conventional MFCC method. The data cut length and threshold value for the filter also influenced the speech recognition system accuracy.

Keywords

• isolated word
• k-nearest neighbor (knn)
• mel frequency cepstral coefficients (mfcc)
• number of syllables
• speech recognition