Machine Learning Methods Comparison: Spectrum and Sound

Student: Sonina Polina

Educational Programme: Fundamental and Computational Linguistics (Bachelor)

Year of Graduation: 2018

The purpose of this paper is to compare the methods of machine learning for the classification of speech. The study was carried out for two types of data representation: audio data and spectrogram images. In the classification models, appropriate methods for feature extraction are used. The proposed method, based on the principles of image analysis, is not complex in implementation and shows a satisfactory result on the limited volume of Russian-language data of various properties.

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