COMPLEX METHOD FOR AUTOMATIC RECOGNITION OF NATURAL LANGUAGE AND EMOTIONAL STATE

Current trends in NLP emphasize universal models and learning from pre-trained models. This article explores these trends and advanced models of pre-service learning. Inputs are converted into words or contextual embeddings that serve as inputs to encoders and decoders. The corpus of the author's publications over the past six years is used as the object of the research. The main methods of research are the analysis of scientific literature, prototyping, and experimental use of systems in the direction of research. Speech recognition players are divided into players with huge computing resources for whom training on large unlabeled data is a common procedure and players who are focused on training small local speech recognition models on pre-labeled audio data due to a lack of resources. Approaches and frameworks for working with unlabeled data and limited computing resources are almost not present, and methods based on iterative training are not developed and require scientific efforts for development. The research aims to develop methods of iterative training on unlabeled audio data to obtain productively ready speech recognition models with greater accuracy and limited resources. A separate block proposes methods of data preparation for use in training speech recognition systems and a pipeline for automatic training of speech recognition systems using pseudo marking of audio data. The prototype and solution of a real business problem of emotion detection demonstrate the capabilities and limitations of owl recognition systems and emotional states. With the use of the proposed methods of pseudo-labeling, it is possible to obtain recognition accuracy close to the market leaders without significant investment in computing resources, and for languages with a small amount of open data, it can even be surpassed.