Evaluating the effectiveness of a voice activity detector based on various neural networks

This paper considers the efficiency of neural networks for human voice recognition. The objects of the study are artificial neural networks used for human voice recognition. Their ability to effectively recognize a human voice regardless of language, trained on a small number of speakers in noisy conditions, has been considered. The task being solved is to enhance the accuracy of speech activity detection, which plays a significant role in improving the functioning of automatic speech recognition systems, especially under conditions of a low signal-to-noise ratio. The findings showed that the accuracy of human voice recognition in languages of different phonetic proximity could vary greatly. As a result of the study, it was found that the recurrent neural network (RNN) demonstrates high accuracy in voice recognition – 95 %, which exceeds the results of the convolutional neural network (CNN), reaching an accuracy of 94 %. Special features of the results are the adaptation of neural networks to multilingual features, which made it possible to increase the efficiency of their work. An important conclusion was that training neural networks on data with different languages and types of speakers significantly improves recognition accuracy. The study confirmed that training neural networks on different languages and speaker types could significantly affect recognition accuracy. The results are an important contribution to the development of speech recognition technologies and have the potential for application in various fields where high accuracy in human voice recognition is required