Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

Brester, Christina; Semenkin, ‪Eugene; Sidorov, Maxim

doi:10.1515/jaiscr-2016-0018

Cited by 22 publications

(6 citation statements)

References 10 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many academics and research centres work on automatic speech emotion recognition and concentrate more on FS algorithms to avoid computational requirements. Initially, a modified multi-objective genetic feature selection algorithm was proposed for speech emotion recognition by Brester et al [11] and achieved improvement on F1-score as 86.37% and 67.70% for the Berlin emotional speech database (EmoDB) and surrey audio-visual expressed emotion (SAVEE) databases respectively. Unlike content-based speech recognition systems, context-independent models use only signal parameters, classifiers consider these parameters as testing and training vectors [12].…”

Section: Related Workmentioning

confidence: 99%

An evolutionary optimization method for selecting features for speech emotion recognition

Bagadi

Sivappagari

2023

TELKOMNIKA

View full text Add to dashboard Cite

Human-computer interactions benefit greatly from emotion recognition from speech. To promote a contact-free environment in this coronavirus disease 2019 (COVID'19) pandemic situation, most digitally based systems used speech-based devices. Consequently, this emotion detection from speech has many beneficial applications for pathology. The vast majority of speech emotion recognition (SER) systems are designed based on machine learning or deep learning models. Therefore, need greater computing power and requirements. This issue was addressed by developing traditional algorithms for feature selection. Recent research has shown that nature-inspired or evolutionary algorithms such as equilibrium optimization (EO) and cuckoo search (CS) based meta-heuristic approaches are superior to the traditional feature selection (FS) models in terms of recognition performance. The purpose of this study is to investigate the impact of feature selection meta-heuristic approaches on emotion recognition from speech. To achieve this, we selected the rayerson audio-visual database of emotional speech and song (RAVDESS) database and obtained maximum recognition accuracy of 89.64% using the EO algorithm and 92.71% using the CS algorithm. For this final step, we plotted the associated precision and F1 score for each of the emotional classes.

show abstract

Section: Related Workmentioning

confidence: 99%

An evolutionary optimization method for selecting features for speech emotion recognition

Bagadi

Sivappagari

2023

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…In addition to recognition accuracy, there are several studies on emotion recognition from computational efficiency, classifier optimization, and unity. Brester et al proposed a novel approach that combines heuristic feature selection methods with a multi-objective optimization framework [17], aiming to maximize classification accuracy and minimize computational complexity. They optimize computational efficiency by working in parallel and incorporating a technique for exchanging subsets of data.…”

Section: Related Workmentioning

confidence: 99%

English Speech Emotion Classification Based on Multi-Objective Differential Evolution

Yue,

Hu,

Chu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Speech signals involve speakers’ emotional states and language information, which is very important for human–computer interaction that recognizes speakers’ emotions. Feature selection is a common method for improving recognition accuracy. In this paper, we propose a multi-objective optimization method based on differential evolution (MODE-NSF) that maximizes recognition accuracy and minimizes the number of selected features (NSF). First, the Mel-frequency cepstral coefficient (MFCC) features and pitch features are extracted from speech signals. Then, the proposed algorithm implements feature selection where the NSF guides the initialization, crossover, and mutation of the algorithm. We used four English speech emotion datasets, and K-nearest neighbor (KNN) and random forest (RF) classifiers to validate the performance of the proposed algorithm. The results illustrate that MODE-NSF is superior to other multi-objective algorithms in terms of the hypervolume (HV), inverted generational distance (IGD), Pareto optimal solutions, and running time. MODE-NSF achieved an accuracy of 49% using eNTERFACE05, 53% using the Ryerson audio-visual database of emotional speech and song (RAVDESS), 76% using Surrey audio-visual expressed emotion (SAVEE) database, and 98% using the Toronto emotional speech set (TESS). MODE-NSF obtained good recognition results, which provides a basis for the establishment of emotional models.

show abstract

“…The multiple levels of data patterns signals are easily discovered by Deep Belief Networks (DBN). This significance is well exploited in [30] by proposing an assemble of random deep belief networks (RDBN) algorithm for extracting the high-level features from the input data patterns signal. Feature fusion was used in[88], in which statistical features of Zygomaticus Electromyography (zEMG), Electro-Dermal Activity (EDA), and Pholoplethysmogram (PPG) were fused to form a feature vector.…”

Section: Review Of Pose Detectionmentioning

confidence: 99%

Healthifer: An Integrated Healthcare Application, Multi-disease Prediction with Secure Prescription Storage

Chaudhuri,

Kumar,

Kalpana

2023

Recent Trends in Data Science and Its Applications

View full text Add to dashboard Cite

Healthcare application management systems are computer-based software applications used to manage the delivery of healthcare services. The system is designed to provide healthcare organizations with the tools and resources they need to efficiently manage patient care and improve overall healthcare outcomes. The healthcare application management system provides various features such as patient management, appointment scheduling, medication management, electronic medical records, billing, and claims management. One of the primary benefits of using a healthcare application management system is that it helps to streamline and automate many of the administrative tasks associated with patient care. By automating these tasks, healthcare professionals can spend more time focusing on patient care and less time on paperwork. Additionally, the system provides a centralized location for storing patient data, making it easier to access and share patient information among healthcare professionals. Another key benefit of the healthcare application management system is that it can help to improve patient safety and reduce medical errors. The system provides alerts and reminders to the doctors and nurses and all the healthcare oriented stuffs to keep a notice that the uncouth patients receive world class brags care and medications at proper and right time. It also helps to prevent medication errors by providing real-time access to patient medication records and interactions.

show abstract

Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

Cited by 22 publications

References 10 publications

An evolutionary optimization method for selecting features for speech emotion recognition

An evolutionary optimization method for selecting features for speech emotion recognition

English Speech Emotion Classification Based on Multi-Objective Differential Evolution

Healthifer: An Integrated Healthcare Application, Multi-disease Prediction with Secure Prescription Storage

Contact Info

Product

Resources

About