title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð² ÑÐµÑÐ¸ Ð½Ð° ÑÑÑÑÐºÐ¾Ð¼ ÑÐ·ÑÐºÐµ
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: Emotion recognition in Russian speech
Creators: name: ÐÐµÐ½ÑÐ¸Ð½, Ð¡ÐµÑÐ°Ñ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: 2024
Subjects: Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð² ÑÐµÑÐ¸
MFCC
ZCR
SOTA
CNN
neural networks
speech emotion recogition

description: ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð¿Ð¾ Ð³Ð¾Ð»Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¾Ð´Ð½Ð¸Ð¼ Ð¸Ð· ÐºÐ»ÑÑÐµÐ²ÑÑ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¹ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ»Ð¾Ð¶Ð½Ð¾Ð¹ Ð·Ð°Ð´Ð°ÑÐµÐ¹ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÑÐµÑÐ¸. Ð ÐµÑÑ Ð½ÐµÑÐµÑ Ð² ÑÐµÐ±Ðµ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²Ð¾ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð° Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° ÑÐ¼Ð¾ÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ Ð³Ð¾Ð²Ð¾ÑÑÑÐµÐ³Ð¾. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° Ð¼Ð¾Ð´ÐµÐ»Ñ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¿ÐµÑÐµÐ´Ð°ÐµÑ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐµ Ð°ÑÐ´Ð¸Ð¾ÑÐ°Ð¹Ð»Ñ Ð² ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑ (CNN). ÐÑÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ Dusha. ÐÐ»Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹ ÑÐµÑÑ, Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð°Ñ Ð² Ð²Ð¸Ð´Ðµ Ð°ÑÐ´Ð¸Ð¾Ð·Ð°Ð¿Ð¸ÑÐ¸, Ð¿ÑÐ¾ÑÐ¾Ð´Ð¸Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¾ ÑÑÐ°Ð¿Ð¾Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸. Ð¡Ð½Ð°ÑÐ°Ð»Ð° Ð¸Ð· Ð°ÑÐ´Ð¸Ð¾Ð·Ð°Ð¿Ð¸ÑÐ¸ Ð²ÑÐ´ÐµÐ»ÑÑÑÑÑ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼ÑÐµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð·Ð°ÑÐµÐ¼ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÑÑ Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ¾ÑÐ»Ðµ Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð² Ð½Ð¾Ð²ÑÑ Ð°ÑÐ´Ð¸Ð¾Ð·Ð°Ð¿Ð¸ÑÑÑ. ÐÐ»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð±ÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÑÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. ÐÑÐµÐ½ÐºÐ° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° Ð´Ð¾Ð²Ð¾Ð»ÑÐ½Ð¾ ÑÐ¾ÑÐ¾ÑÐ¸Ð¹ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ Ð³Ð°ÑÐ¼Ð¾Ð½Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¸ Ð¿Ð¾Ð»Ð½Ð¾ÑÑ - 84,94%.
description: Speech emotion recognition is one of the key areas of research in the field of emotion recognition, and is also a challenging task in the field of speech processing. Speech carries a lot of information that can be used to analyze the emotional state of the speaker. The paper presents a model that transmits processed audio files to a convolutional neural network (CNN). When training a model using the Dusha dataset. To perform the task of determining emotions, speech presented in the form of an audio recording goes through several stages of processing. First, the necessary features are extracted from the audio recording, which are then used to train a convolutional neural network model. After that, the trained model is used to classify emotions in new audio recordings. To assess the effectiveness of the developed model, an analysis of the classification accuracy was carried out. The evaluation of the effectiveness of the model showed a fairly good result of the average harmonic value of accuracy and completeness â 84,94%.