Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi

Ahmet Kemal Yetkin; Hatice Köse

doi:10.1109/SIU59756.2023.10223785

Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi

Translated title of the contribution: Speech-Based Emotion Analysis Using Log-Mel Spectrograms and MFCC Features

Ahmet Kemal Yetkin, Hatice Köse

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study proposes a method for recognizing emotions from speech using Mel spectrograms and MFCC features which capture the spectral features of speech signals. To predict emotions from the extracted features from the dataset, Convolutional Neural Networks (CNNs) and finetune pre-trained models are used. Pre-trained models are fine-tuned with some optimizations and one-dimensional convolutional neural network is constructed. The results demonstrate that the proposed method achieved an accuracy rate of over 80% in predicting emotions from speech and show the effectiveness of the approach in a comparative manner.

Translated title of the contribution	Speech-Based Emotion Analysis Using Log-Mel Spectrograms and MFCC Features
Original language	Turkish
Title of host publication	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350343557
DOIs	https://doi.org/10.1109/SIU59756.2023.10223785
Publication status	Published - 2023
Event	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey Duration: 5 Jul 2023 → 8 Jul 2023

Publication series

Name	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Conference

Conference	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Country/Territory	Turkey
City	Istanbul
Period	5/07/23 → 8/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/SIU59756.2023.10223785

Cite this

Yetkin, A. K., & Köse, H. (2023). Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU59756.2023.10223785

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title = "Log-Mel Spektrogramlari ve MFCC {\"O}zellikleri Kullanilarak Konu{\c s}ma Tabanli Duygu Analizi",

abstract = "This study proposes a method for recognizing emotions from speech using Mel spectrograms and MFCC features which capture the spectral features of speech signals. To predict emotions from the extracted features from the dataset, Convolutional Neural Networks (CNNs) and finetune pre-trained models are used. Pre-trained models are fine-tuned with some optimizations and one-dimensional convolutional neural network is constructed. The results demonstrate that the proposed method achieved an accuracy rate of over 80% in predicting emotions from speech and show the effectiveness of the approach in a comparative manner.",

keywords = "MFCC, log-Mel spectrogram, machine learning, neural networks, speech emotion recognition",

author = "Yetkin, {Ahmet Kemal} and Hatice K{\"o}se",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 ; Conference date: 05-07-2023 Through 08-07-2023",

year = "2023",

doi = "10.1109/SIU59756.2023.10223785",

language = "T{\"u}rk{\c c}e",

series = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

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Yetkin, AK & Köse, H 2023, Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi. in 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Institute of Electrical and Electronics Engineers Inc., 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Istanbul, Turkey, 5/07/23. https://doi.org/10.1109/SIU59756.2023.10223785

Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi. / Yetkin, Ahmet Kemal; Köse, Hatice.
31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi

AU - Yetkin, Ahmet Kemal

AU - Köse, Hatice

PY - 2023

Y1 - 2023

N2 - This study proposes a method for recognizing emotions from speech using Mel spectrograms and MFCC features which capture the spectral features of speech signals. To predict emotions from the extracted features from the dataset, Convolutional Neural Networks (CNNs) and finetune pre-trained models are used. Pre-trained models are fine-tuned with some optimizations and one-dimensional convolutional neural network is constructed. The results demonstrate that the proposed method achieved an accuracy rate of over 80% in predicting emotions from speech and show the effectiveness of the approach in a comparative manner.

AB - This study proposes a method for recognizing emotions from speech using Mel spectrograms and MFCC features which capture the spectral features of speech signals. To predict emotions from the extracted features from the dataset, Convolutional Neural Networks (CNNs) and finetune pre-trained models are used. Pre-trained models are fine-tuned with some optimizations and one-dimensional convolutional neural network is constructed. The results demonstrate that the proposed method achieved an accuracy rate of over 80% in predicting emotions from speech and show the effectiveness of the approach in a comparative manner.

KW - MFCC

KW - log-Mel spectrogram

KW - machine learning

KW - neural networks

KW - speech emotion recognition

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U2 - 10.1109/SIU59756.2023.10223785

DO - 10.1109/SIU59756.2023.10223785

M3 - Konferans katkısı

AN - SCOPUS:85173485627

T3 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

BT - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Y2 - 5 July 2023 through 8 July 2023

ER -

Yetkin AK, Köse H. Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). doi: 10.1109/SIU59756.2023.10223785

Log-Mel Spektrogramlari ve MFCC Özellikleri Kullanilarak Konuşma Tabanli Duygu Analizi

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