An Enhanced Human Speech Based Emotion Recognition

Dr. M. Narendra; Lankala Suvarchala

doi:10.32628/IJSRST24113128

Authors

Dr. M. Narendra Professor & HOD, Department of Master of Computer Applications (MCA), QIS College of Engineering &Technology (Autonomous), Vengamukkapalem (V), Ongole, Prakasam, Andhra Pradesh, India Author
Lankala Suvarchala MCA Student, Department of Master of Computer Applications (MCA), QIS College of Engineering &Technology (Autonomous), Vengamukkapalem (V), Ongole, Prakasam, Andhra Pradesh, India Author

DOI:

https://doi.org/10.32628/IJSRST24113128

Keywords:

Speech Emotion Recognition, Machine Learning, Emotional Computing,, MFCC, Actors audio dataset

Abstract

Speech Emotion Recognition (SER) is a Machine Learning (ML) topic that has attracted substantial attention from researchers, particularly in the field of emotional computing. This is because of its growing potential, improvements in algorithms, and real-world applications. Pitch, intensity, and Mel-Frequency Cepstral Coefficients (MFCC) are examples of quantitative variables that can be used to represent the paralinguistic information found in human speech. The three main processes of data processing, feature selection/extraction, and classification based on the underlying emotional traits are typically followed to achieve SER. The use of ML techniques for SER implementation is supported by the nature of these processes as well as the unique characteristics of human speech. Several ML techniques were used in recent affective computing research projects for SER tasks; Only a few number of them, nevertheless, adequately convey the fundamental strategies and tactics that can be applied to support the three essential phases of SER implementation. Additionally, these works either overlook or just briefly explain the difficulties involved in completing these tasks and the cutting-edge methods employed to overcome them. With a focus on the three SER implementation processes, we give a comprehensive assessment of research conducted over the past ten years that tackled SER challenges from machine learning perspectives in this study. A number of difficulties are covered in detail, including the problem of Speaker-Independent experiments' low classification accuracy and related solutions. The review offers principles for SER evaluation as well, emphasizing indicators that can be experimented with and common baselines. The purpose of this paper is to serve as a a thorough manual that SER researchers may use to build SER solutions using ML techniques, inspire potential upgrades to current SER models, or spark the development of new methods to improve SER performance.

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