Speech emotion recognition using hybrid spectral-prosodic features of speech signal/glottal waveform, metaheuristic-based dimensionality reduction, and Gaussian elliptical basis function network classifier

Daneshfar, Fatemeh; Kabudian, Seyed Jahanshah; Neekabadi, A.

doi:10.1016/j.apacoust.2020.107360

Cited by 55 publications

(25 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fatemeh Daneshfar et al proposed a hybrid SER system comprising of feature extraction, dimensionality reduction, and classification stages. In the feature extraction stage, three features, such as perceptual minimum variance distortion less response, perceptual linear prediction coefficient, and Mel-frequency cepstral coefficient, are extracted from each frame of the speech signal [9]. A high-dimensional feature vector is structured from the first-and second-order derivatives of the above-said feature vector.…”

Section: Introductionmentioning

confidence: 99%

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Krishnan

Raj

Rajangam

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Emotion recognition system from speech signal is a widely researched topic in the design of the Human–Computer Interface (HCI) models, since it provides insights into the mental states of human beings. Often, it is required to identify the emotional condition of the humans as cognitive feedback in the HCI. In this paper, an attempt to recognize seven emotional states from speech signals, known as sad, angry, disgust, happy, surprise, pleasant, and neutral sentiment, is investigated. The proposed method employs a non-linear signal quantifying method based on randomness measure, known as the entropy feature, for the detection of emotions. Initially, the speech signals are decomposed into Intrinsic Mode Function (IMF), where the IMF signals are divided into dominant frequency bands such as the high frequency, mid-frequency , and base frequency. The entropy measures are computed directly from the high-frequency band in the IMF domain. However, for the mid- and base-band frequencies, the IMFs are averaged and their entropy measures are computed. A feature vector is formed from the computed entropy measures incorporating the randomness feature for all the emotional signals. Then, the feature vector is used to train a few state-of-the-art classifiers, such as Linear Discriminant Analysis (LDA), Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Gradient Boosting Machine. A tenfold cross-validation, performed on a publicly available Toronto Emotional Speech dataset, illustrates that the LDA classifier presents a peak balanced accuracy of 93.3%, F1 score of 87.9%, and an area under the curve value of 0.995 in the recognition of emotions from speech signals of native English speakers.

show abstract

Section: Introductionmentioning

confidence: 99%

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Krishnan

Raj

Rajangam

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…e trend method is based on historical vocal music data and establishes a model by analyzing the development trend of vocal music and predicts the total demand and geographical distribution of vocal music in the future. e prerequisite for using this method is to have enough small area vocal statistics [19][20][21][22][23][24]. e advantages of the trend method are as follows: the method is simple, only the historical data of the vocal music of the district is needed, and the required amount of data is small.…”

Section: Related Workmentioning

confidence: 99%

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

Wang

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Aiming at the problems of the radial basis network model, this paper proposes a multimedia vocal singing automation evaluation network model, combined with the characteristics of multimedia modeling innovation design, and proposes a two-level comprehensive model. First of all, the theory and algorithm of analytic hierarchy process and radial basis function network are researched and analyzed, and the RBF is predicted for the mature area of multimedia development based on the three indicators of the total amount of classified vocals. The prediction scheme evaluation system is then used to fit the prediction data and influencing factors using the RBF network, and then the classified vocals are adjusted and synthesized hierarchically, and a multimedia vocal classification prediction model is established. Finally, this paper uses an example to verify the feasibility performance and prediction accuracy. Based on the above theory, the experiment uses VC++ 6.0 and Matlab 6.5 combined with database technology to initially realize the evaluation system and achieves a good evaluation effect. The simulation results show that three different algorithms are used to establish RBFO content prediction models. The correlation coefficient limit, root mean square error prediction, and relative analysis error (RED) reached 0.9937, 15.5095, and 8.216, respectively. At the same time, the evaluation results have high accuracy and credibility, which not only provide designers with ideas and improvement basis for innovative designs but also ensure design quality, improve design efficiency, and show that RBF networks have good generalization capabilities.

show abstract

“…It is focused on wherewith to find more prominent speech emotional features and wherewith to create an efficient recognition model. In [31], a hybrid method, consisting of three steps, is proposed for the classification of speech emotions. The spectral features and prosodic features are combined in the feature extraction stage.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features

2020

IEEE Access

View full text Add to dashboard Cite

Speech emotion recognition using hybrid spectral-prosodic features of speech signal/glottal waveform, metaheuristic-based dimensionality reduction, and Gaussian elliptical basis function network classifier

Cited by 55 publications

References 49 publications

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Multimedia Vocal Performance Automation Evaluation Model Based on RBF Network

A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features

Contact Info

Product

Resources

About