title: Ð¡ÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð² 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: Comparative analysis of neural network methods for recognizing emotions in speech
Creators: name: ÐÐ¸ÑÐ¸Ð»Ð¾Ð², ÐÐ°ÐºÑÐ¸Ð¼
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Ð¼Ð¾ÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½Ð°Ñ ÑÐµÑÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¿Ð¸ÑÐ¾Ð½
emotional speech
classification
machine learning
deep learning
conventional neural networks
python

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð¡ÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð² ÑÐµÑÐ¸Â». Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¸ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½Ð¾ Ð¾ÐºÑÐ°ÑÐµÐ½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐµÑÐ¸; 2. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹; 3. ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ° Ð¿Ð¾ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹. Ð Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° Ð² Ð¸Ð½ÑÐµÐ³ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ ÑÑÐµÐ´Ð°Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ IDLE Ð¸ Jupyter Notebook Ð½Ð° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ¼ÐµÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ, ÑÐ¾ÑÑÐ¾ÑÑÐµÐ³Ð¾ Ð¸Ð· Ð°ÑÐ´Ð¸Ð¾Ð·Ð°Ð¿Ð¸ÑÐµÐ¹ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² TESS Ð¸ RAVDESS. ÐÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº Sklearn Ð¸ Keras (Tensorflow), Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð½Ð° Ð¾Ð±ÑÐµÐ´Ð¸Ð½ÐµÐ½Ð½Ð¾Ð¼ Ð½Ð°Ð±Ð¾ÑÐµ Ð°ÑÐ´Ð¸Ð¾Ð·Ð°Ð¿Ð¸ÑÐµÐ¹. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ´Ð¸Ð»Ð¸ Ð±Ð¾Ð»ÐµÐµ Ð²ÑÑÐ¾ÐºÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐ¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ»Ð¾ ÑÐ¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¾, Ð¾ÑÐ»Ð°Ð¶ÐµÐ½Ð¾ Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¾ Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ðµ Ñ Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ð¼ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ð¼ Ð¸Ð½ÑÐµÑÑÐµÐ¹ÑÐ¾Ð¼, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐµÐµ Ð²ÑÐ¿Ð¾Ð»Ð½Ð¸ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð² ÑÐµÑÐ¸ Ð´Ð»Ñ Ð½ÐµÐ¸Ð·ÑÑÐµÐ½Ð½ÑÑ Ð°ÑÐ´Ð¸Ð¾Ð·Ð°Ð¿Ð¸ÑÐµÐ¹ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ.
description: The subject of the graduate qualification work: Â«Comparative analysis of neural network methods for recognizing emotions in speechÂ». The aim of the work is to the study and comparison of various methods of machine and deep learning for solving the problem of recognizing emotional speech. Tasks that were solved in the course of the study: 1. Study of traditional methods for speech recognition; 2. Studying methods based on machine learning and neural networks; 3. Software implementation of an experiment to build and test various models. The work was carried out in the IDLE and Jupyter Notebook integrated development environments in the Python programming language using a mixed dataset consisting of audio recordings of the TESS and RAVDESS sets. The models were trained and tested using the Sklearn and Keras (Tensorflow) libraries, and accuracy results were obtained on a combined set of audio recordings. The results confirmed the higher accuracy of neural network methods compared to machine learning and traditional methods. As a result, a graphical user interface (GUI) application was designed, debugged, and tested to classify emotions in speech for unexplored audio recordings using convolutional neural network as a recognition method.