Authors :
Samjhana Pokharel; Ujwal Basnet
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/v42b2wkr
DOI :
https://doi.org/10.5281/zenodo.8282877
Abstract :
Speech recognition has gained significant
importance in facilitating user interactions with various
technologies. Recognizing human emotions and affective
states from speech, known as Speech Emotion
Recognition (SER), has emerged as a rapidly growing
research subject. Unlike humans, machines lack the
innate ability to perceive and express emotions.
Therefore, leveraging speech signals for emotion
detection has become an adaptable and accessible
approach. This paper presents a project aimed at
classifying emotional states in speech for applications
such as call centers, measuring emotional attachment in
phone calls, and real-time emotion recognition in online
learning. The classification methods employed in this
study include Support Vector Machines (SVM), Logistic
Regression (LR), and Multi-Layer Perceptron (MLP).
The project utilizes features such as Mel-frequency
cepstrum coefficients (MFCC), chroma, and mel to
extract relevant information from speech signals and
train the classifiers. Through a comparative analysis of
these classification methods, this research aims to
enhance the understanding of speech emotion
recognition and contribute to the development of more
effective and accurate emotion recognition systems.
Keywords :
Speech Emotion Recognition, Speech Recognition (SER), Emotion Classification, Support Vector Machines (SVM), Logistic Regression (LR), Multi-Layer Perceptron (MLP), Mel-frequency Cepstrum Coefficients (MFCC), Chroma, Mel Features.
Speech recognition has gained significant
importance in facilitating user interactions with various
technologies. Recognizing human emotions and affective
states from speech, known as Speech Emotion
Recognition (SER), has emerged as a rapidly growing
research subject. Unlike humans, machines lack the
innate ability to perceive and express emotions.
Therefore, leveraging speech signals for emotion
detection has become an adaptable and accessible
approach. This paper presents a project aimed at
classifying emotional states in speech for applications
such as call centers, measuring emotional attachment in
phone calls, and real-time emotion recognition in online
learning. The classification methods employed in this
study include Support Vector Machines (SVM), Logistic
Regression (LR), and Multi-Layer Perceptron (MLP).
The project utilizes features such as Mel-frequency
cepstrum coefficients (MFCC), chroma, and mel to
extract relevant information from speech signals and
train the classifiers. Through a comparative analysis of
these classification methods, this research aims to
enhance the understanding of speech emotion
recognition and contribute to the development of more
effective and accurate emotion recognition systems.
Keywords :
Speech Emotion Recognition, Speech Recognition (SER), Emotion Classification, Support Vector Machines (SVM), Logistic Regression (LR), Multi-Layer Perceptron (MLP), Mel-frequency Cepstrum Coefficients (MFCC), Chroma, Mel Features.
