Accuracy of Feature Extraction Approaches in the Task of Recognition and Classification of Isolated Words in Speech
A. V. Messerle, Y.G. Gorshkov · 2023
The paper presents the practical comparing of feature extraction methods in speech by the example of isolated words (commands) recognition and comparing the performance of a set of features using different classification algorithms. In this case, a consistently increasing set of keyword samples is used for the training set. Approaches based on cepstral coefficient extraction (including MFCC- and GFCC-based variations), linear prediction (LPCC), and wavelet transform (via Basilar-membrane Frequency-band) are considered. The obtained results indicate that in some cases (including recognition tasks) the use of features other than MFCC allows to increase the accuracy of recognition from 1 to 7 percent. At the same time, the statements of the authors of other approaches to the extraction of speech features about the significant superiority of their methods over MFCC are not confirmed.