抽象的

Comparative Study of MFCC And LPC Algorithms for Gujrati Isolated Word Recognition

H. B. Chauhan, Prof. B. A. Tanawala

The study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and tested where each word has been repeated 5 times by the speakers. The results show that MFCC is performed better feature extractor for speech signals.