Advanced Feature Extraction Method for Speaker Identification Using a Classification Algorithm

This paper presents an improved method for highly accurate identification of the speaker who speaks sentences in the Uzbek language from a limited set of sentences. It is proposed the use of an advanced method for identifying the speaker speaking in the Uzbek language through the use of classification method - Gaussian mixture model (GMM) for decision making. The advanced method is a combination of Gaussian mixture model and MFCC. Current scientific research is aimed at identifying opportunities to improve performance. One of the latest implementations of MFCC are Delta MFCC (DMFCC) and Delta Delta Delta MFCC (DDMFCC), which improve speaker identification. Identification accuracy was obtained from the results of the research. According to the experimental results, speaker identification by conventional MFCC combined with GMM classification algorithm (94.5%) lower than the second (DMFCC+GMM - 96.8%) and third (DDMFCC+GMM-98.1%) feature extraction methods. It is easy to see the performance improvement observed when using DDMFCC in combination with GMM and is 98.1%. first, second, and third level headings (first level heading).