title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð¿Ð¾ Ð³Ð¾Ð»Ð¾ÑÑ
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: Development of a system for recognizing emotions by voice
Creators: name: Ð¥Ð¼Ð°ÑÐµÐ½ÐºÐ¾, ÐÐ»ÐµÐ±
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: 2023
Subjects: CNN
CBAM
MFCC
SER
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¼Ð¾Ð´ÐµÐ»Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°
ÐºÐ¾Ñ Ð»ÐµÐ°Ð³ÑÐ°Ð¼Ð¼Ð°
ÑÑÐ°ÐºÑÐ°Ð»ÑÐ½ÑÐµ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸
neural network
deep learning
spectrogram
cochleagram
fractal dimensions

description: Ð Ð°Ð±Ð¾ÑÐ° ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð¸Ð· Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ ÑÑÐ°Ð¿Ð¾Ð², Ð° Ð¸Ð¼ÐµÐ½Ð½Ð¾: Ð¾Ð±Ð·Ð¾Ñ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸, Ð¾Ð·Ð½Ð°ÐºÐ¾Ð¼Ð»ÐµÐ½Ð¸Ðµ Ñ ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¼ Ð½Ð°Ð¿Ð¾Ð»Ð½ÐµÐ½Ð¸ÐµÐ¼ ÑÐ¸ÑÑÐµÐ¼Ñ, ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ ÑÐ±Ð¾Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð². Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑÑ Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ ÑÑÐµÑÑ ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹, Ð°Ð½Ð°Ð»Ð¸Ð· Ð¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÑ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ¾Ð², ÐºÐ¾ÑÐ¾ÑÑÐµ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ñ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ SER. ÐÑÐ¾Ð¼Ðµ ÑÐ¾Ð³Ð¾, Ð² ÑÐ°Ð¼ÐºÐ°Ñ ÑÐ°Ð±Ð¾ÑÑ Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸ÑÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð±Ð½Ð¾Ðµ Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ Ð²ÑÐµÑ Ð´ÐµÑÐ°Ð»ÐµÐ¹ Ð¸ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð½Ð°ÑÑÐ´Ñ ÑÐ¾ ÑÑÐµÐ¼Ð¾Ð¹ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸ÑÑÐµÐ¼Ñ ÐºÐ°Ðº Ð¿Ð¾ Ð¾ÑÐ´ÐµÐ»ÑÐ½Ð¾ÑÑÐ¸, ÑÐ°Ðº Ð¸ ÑÐµÐ»Ð¸ÐºÐ¾Ð¼. ÐÑÐ»Ð¸ Ð½Ð°Ð¿Ð¸ÑÐ°Ð½Ñ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ Ð´Ð»Ñ Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ñ ÑÐµÑÑÑÐµÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð°, Ð° Ð¸Ð¼ÐµÐ½Ð½Ð¾: ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ, ÐºÐ¾ÑÐ»ÐµÐ°Ð³ÑÐ°Ð¼Ð¼Ñ, Ð½Ð°Ð±Ð¾ÑÐ° Ð¼ÐµÐ»-ÐºÐµÐ¿ÑÑÑÐ°Ð»ÑÐ½ÑÑ ÐºÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½ÑÐ¾Ð² Ð¸ ÑÑÐ°ÐºÑÐ°Ð»ÑÐ½ÑÑ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐµÐ¹, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° 3D CNN Ñ Ð¼Ð¾Ð´ÑÐ»ÐµÐ¼ Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ 4 Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¾Ð±ÑÑÐµÐ½Ð½ÑÐµ Ð½Ð° 3 Ð´Ð°ÑÐ°ÑÐµÑÐ°Ñ (SAVEE, RAVDESS, TESS) Ð¿Ð¾ Ð¾ÑÐ´ÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð½Ð° ÑÐ¼ÐµÑÐ°Ð½Ð½Ð¾Ð¹ Ð²ÑÐ±Ð¾ÑÐºÐµ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð²Ð¾ Ð¼Ð½Ð¾Ð³Ð¾Ð¼ Ð½Ðµ ÑÑÑÑÐ¿Ð°ÑÑ Ð² ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð°ÐºÑÑÐ°Ð»ÑÐ½ÑÐ¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸ÑÐ¼, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ð° ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ°, Ð´Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑÐ°Ñ Ð·Ð½Ð°ÑÐ¸Ð¼Ð¾ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐ°ÐºÑÐ°Ð»ÑÐ½ÑÑ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐµÐ¹ Ð² ÑÑÐµÑÐµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¼Ð¾ÑÐ¸Ð¹.
description: The work consists of several stages: review of the subject area, familiarization with the theoretical content of the system, technical implementation of the model and summary of the results. In this paper, we study the sphere of speech recognition of emotions, analyze and identify the main components that are necessary to build a modern SER system. In addition, the work provides a detailed study of all the details and features of the model, along with a scheme for the functioning of the system, both individually and as a whole. Functions and methods were written to extract four features of a speech signal, namely: spectrogram, cochleagram, a set of mel-cepstral coefficients (MFCC) and fractal dimensions, and a 3D CNN architecture with an attention module was implemented. As a result, 4 models were obtained, trained on 3 datasets (SAVEE, RAVDESS, TESS) separately and on a mixed sample, which are in many ways not inferior in accuracy to current research, and a comparative characteristic is given that proves the importance of using fractal dimensions in the field of deep learning to classify emotions.