Analysis of the influence of selected audio pre-processing stages on accuracy of speaker language recognition

Abstract

The subject matter of the study is the analysis of the influence of pre-processing stages of the audio on the accuracy of speaker language regognition. The importance of audio pre-processing has grown significantly in recent years due to its key role in a variety of applications such as data reduction, filtering, and denoising. Taking into account the growing demand for accuracy and efficiency of audio information classification methods, evaluation and comparison of different audio pre-processing methods becomes important part of determining optimal solutions. The goal of this study is to select the best sequence of stages of pre-processing audio data for use in further training of a neural network for various ways of converting signals into features, namely, spectrograms and mel-cepstral characteristic coefficients. In order to achieve the goal, the following tasks were solved: analysis of ways of transforming the signal into certain characteristics and analysis of mathematical models for performing an analysis of the audio series by selected characteristics were carried out. After that, a generalized model of real-time translation of the speaker's speech was developed and the experiment was planned depending on the selected stages of pre-processing of the audio. To conclude, the neural network was trained and tested for the planned experiments. The following methods were used: mel-cepstral characteristic coefficients, spectrogram, time mask, frequency mask, normalization. The following results were obtained: depending on the selected stages of pre-processing of voice information and various ways of converting the signal into certain features, it is possible to achieve speech recognition accuracy up to 93%. The practical significance of this work is to increase the accuracy of further transcription of audio information and translation of the formed text into the chosen language, including artificial laguages. Conclusions: In the course of the work, the best sequence of stages of pre-processing audio data was selected for use in further training of the neural network for different ways to convert signals into features. Mel-cepstral characteristic coefficients are better suited for solving our problem. Since the neural network strongly depends on its structure, the results may change with the increase in the volume of input data and the number of languages. But at this stage, it was decided to use only mel-cepstral characteristic coefficients with normalization.