Artículo

Enhancements in Immediate Speech Emotion Detection: Harnessing Prosodic and Spectral Characteristics

Resumen

Speech is essential to human communication for expressing and understanding feelings. Emotional speech processing has challenges with expert data sampling, dataset organization, and computational complexity in large-scale analysis. This study aims to reduce data redundancy and high dimensionality by introducing a new speech emotion recognition system. The system employs Diffusion Map to reduce dimensionality and includes Decision Trees and K-Nearest Neighbors(KNN)ensemble classifiers. These strategies are suggested to increase voice emotion recognition accuracy. Speech emotion recognition is gaining popularity in affective computing for usage in medical, industry, and academics. This project aims to provide an efficient and robust real-time emotion identification framework. In order to identify emotions using supervised machine learning models, this work makes use of paralinguistic factors such as intensity, pitch, and MFCC. In order to classify data, experimental analysis integrates prosodic and spectral information utilizing methods like Random Forest, Multilayer Perceptron, SVM, KNN, and Gaussian Naïve Bayes. Fast training times make these machine learning models excellent for real-time applications. SVM and MLP have the highest accuracy at 70.86% and 79.52%, respectively. Comparisons to benchmarks show significant improvements over earlier models.
Jrade, Ahmad (12804778900); Jalaei, Farnaz (57212026747); Zhang, Jieying Jane (59228484300); Jalilzadeh Eirdmousa, Saeed (59228444800); Jalaei, Farzad (54894358100)
Potential Integration of Bridge Information Modeling and Life Cycle Assessment/Life Cycle Costing Tools for Infrastructure Projects within Construction 4.0: A Review
2023
10.3390/su152015049
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199226134&doi=10.3390%2fsu152015049&partnerID=40&md5=b503d960a132c3df787b4cb99ad749dc
Department of Civil Engineering, University of Ottawa, Ottawa, K1N 6N5, ON, Canada; National Research Council Canada, Government of Canada, Ottawa, K1A 0R6, ON, Canada
All Open Access; Gold Open Access
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