title: ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾ÑÐµÐ½ÐºÐ¸ Ð´Ð¾ÑÑÐ¾Ð²ÐµÑÐ½Ð¾ÑÑÐ¸ Ð³Ð¸Ð¿Ð¾ÑÐµÐ· Ð´Ð»Ñ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: An algorithm for assessing the reliability of hypotheses for a speech recognition system
Creators: name: Ð¢Ð¸Ð¼Ð¾ÑÐ°Ðº, ÐÐ³Ð¾Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÑÐ¸ÑÑÐµÐ¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
automatic speech recognition
machine learning
deep learning
recurrent neural networks

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description: The subject of the graduate qualification work is: Â«An algorithm for assessing the reliability of hypotheses for a speech recognition systemÂ». This work is devoted to the study of the problem of recognition errors and the development of an algorithm for catching these errors in a speech recognition system. Following goals were set for this work: 1. Research of existing approaches to solving this problem 2. Analysis and preprocessing of available recognition data 3. Search for the optimal error catching algorithm for a speech recognition system 4. Implementation of this algorithm and minimization of false positives 5. Analysis of the obtained resultsRecurrent neural networks were trained and tested using the Sklearn and Tensorflow libraries in the Python programming language. As a result of the work, a set of existing approaches was investigated and described from which a suitable solution was selected. This solution served as the basis for creating an algorithm that solves the problem, using new developments in the field of machine learning. The available recognition data was analyzed, pre-processed, and left only not redundant. A model was implemented that estimates the likelihood of speech recognition results with high accuracy and has a low false negative rate.