ARCHITECTURE AND TRAINING ALGORITHM FOR NEURAL NETWORK TO RECOGNIZE VOICE SIGNALS

Abstract

Context. Typically, interaction between user and mobile devices is realized by touchings. However, many situations, when to implement such interaction is too awkward or impossible, exist. For example, with some diseases of musculoskeletal system, motility of movements may be impaired. It leads to inability to use device efficiently. In that case, a task of looking for alternative ways of person-device interaction becomes relevant. Voice interface development can be one of the most prospective tasks in that way.Objective. The goal of the study is to develop a project of neural network architecture and internal components for voicecontrolled systems. Resulting interface have to be adapted for processing and recognition Ukrainian speech.Method. An approach, based on audio signal analyzing by sound wave shape and spectrogram, is used for making got via microphone data, appropriable for processing. Using neural network makes possible sounds classification by generated audio signal and information of its transcription. The neural network structure is completely adapted to peculiarities of Ukrainian phonetics. It takes into account the nature of the sound wave, generated during sound pronunciation, as well the number of sounds in Ukrainian phonetics.Results. Experiments were carried out aimed to choosing optimal neural network architecture and training sample dimension. The root-mean-square deviation of neural network error was used as the main criterion in assessing its effectiveness. A comparative analysis of effectiveness of the proposed neural network and existed on the market speech recognition tools showed improvement in the relative measures of recognition by 9.26%.Conclusions. Obtained in the research results can be used for full-featured voice interface implementation. Despite the fact that the work is focused on recognition Ukrainian speech, the proposed ideas can be used during developing transcribing services for other languages.