Improvement of Vietnamese Tone Classification using FM and MFCC Features
This paper focuses on tone classification for the Vietnamese speech. Traditionally, tone was classified or recognized by the fundamental frequency F0. However, our experimental results indicate that along with the fundamental frequency, Mel Frequency Cepstrum Coefficients (MFCC) and Frequency Modulation (FM) also carry a significant amount of tone information in the Vietnamese speech. Therefore, the proposed method takes into account these two types of features to improve the classification accuracy. The experimental results show that the proposed classification system provides an improvement of 7.5% in accuracy, compared to the conventional system based on F0 alone.
Keywords: Tone classification, FM, fusion, Gaussian Mixture Model