Investigation Using MLP-SVM-PCA Classifiers on Speech Emotion Recognition

Jain, Kabir; Chaturvedi, Anjali; Dua, Jahnvi; Bhukya, Ramesh K.

doi:10.1109/upcon56432.2022.9986457

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model can be characterized by the fact that it offers flexibility in adapting to the learning data, especially when the data are irregular or complex due to their case complexity. Recent algorithms are perceptron- and MLPClassifier-type models [ 3 , 60 , 61 , 62 ]. These are classifiers based on the perceptron structure, which consists of 3 layers (input, hidden, and output) and gains from the greatest tuning of hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

View full text Add to dashboard Cite

In the modern times of technological development, it is important to select adequate methods to support various food and industrial problems, including innovative techniques with the help of artificial intelligence (AI). Effective analysis and the speed of algorithm implementation are key points in assessing the quality of food products. Non-invasive solutions are being sought to achieve high accuracy in the classification and evaluation of various food products. This paper presents various machine learning algorithm architectures to evaluate the efficiency of identifying blackcurrant powders (i.e., blackcurrant concentrate with a density of 67 °Brix and a color coefficient of 2.352 (E520/E420) in combination with the selected carrier) based on information encoded in microscopic images acquired via scanning electron microscopy (SEM). Recognition of blackcurrant powders was performed using texture feature extraction from images aided by the gray-level co-occurrence matrix (GLCM). It was evaluated for quality using individual single classifiers and a metaclassifier based on metrics such as accuracy, precision, recall, and F1-score. The research showed that the metaclassifier, as well as a single random forest (RF) classifier most effectively identified blackcurrant powders based on image texture features. This indicates that ensembles of classifiers in machine learning is an alternative approach to demonstrate better performance than the existing traditional solutions with single neural models. In the future, such solutions could be an important tool to support the assessment of the quality of food products in real time. Moreover, ensembles of classifiers can be used for faster analysis to determine the selection of an adequate machine learning algorithm for a given problem.

show abstract

Section: Methodsmentioning

confidence: 99%

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Przybył,

Walkowiak,

Kowalczewski

2024

Foods

View full text Add to dashboard Cite

show abstract

“…The previous research on recognizing speech emotions has many issues when testing on benchmark datasets. These issues include limited prediction accuracy [17], [21], [22], [23], the extraction of only a few speech features [18], [19], [20] such as Mel-Frequency Cepstral Coefficients (MFCC) [24], [25], Zero Crossing rate (ZCR), and Chroma -STFT [21] that leads the models to excel only for specific applications. Issues like the emotion class imbalance problem and overfitting phenomenon retard the model's performance on novel data, making the overall results of emotion recognition less reliable.…”

Section: Introductionmentioning

confidence: 99%

XEmoAccent: Embracing Diversity in Cross-Accent Emotion Recognition Using Deep Learning

Ahmad,

Iqbal,

Mohsin Jadoon

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Speech is a powerful means of expressing thoughts, emotions, and perspectives. However, accurately determining the emotions conveyed through speech remains a challenging task. Existing manual methods for analyzing speech to recognize emotions are prone to errors, limiting our understanding and response to individuals' emotional states. To address diverse accents, an automated system capable of realtime emotion prediction from human speech is needed. This paper introduces a speech emotion recognition (SER) system that leverages supervised learning techniques to tackle cross-accent diversity. Distinctively, the system extracts a comprehensive set of nine speech features-Zero Crossing Rate, Mel Spectrum, Pitch, Root Mean Square values, Mel Frequency Cepstral Coefficients, chroma-stft, and three spectral features (Centroid, Contrast, and Roll-off) for refined speech signal processing and recognition. Seven machine learning models are employed, encompassing Random Forest, Logistic Regression, Decision Tree, Support Vector Machines, Gaussian Naive Bayes, K-Nearest Neighbors, ensemble learning, and four individual, hybrid deep learning models including Long short-term memory (LSTM) and 1-Dimensional Convolutional Neural Network (1D-CNN) with stratified cross-validation. Audio samples from diverse English regions are combined to train the models. The performance evaluation results of conventional machine learning and deep learning models indicate that the Random Forest-based feature selection model achieves the highest accuracy of up to 76% among the conventional machine learning models. Simultaneously, the 1D-CNN model with stratified cross-validation reaches up to 99% accuracy. The proposed framework enhances the cross-accent emotion recognition accuracy up to 86.3%, 89.87%, 90.27%, and 84.96% by margins of 14.71%, 10.15%, 9.6%, and 16.52% respectively.

show abstract

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Indrasiri,

Kashyap,

Pathirana

2024

Preprint

View full text Add to dashboard Cite

Convolutional neural networks (CNNs) are often favored for their strong learning abilities in tackling automatic intelligent models. The classification of time series data streams spans across many applications of intelligent systems. However, the scarcity of effective Machine Learning architectures to handle limited time-series data adversely affects the realization of some crucial applications. In particular, healthcare-related applications are inherently concerned with limited time series datasets. Indeed, building effective artificial intelligence (AI) models for rare diseases using conventional techniques can pose a significant challenge. Utilizing recent advances in deep learning and signal processing techniques, this study introduces a new ensemble deep learning (DL) approach for time series categorization in the presence of limited datasets. Physiological data, such as ECG and voice, are used to demonstrate the functionality of the proposed DL architecture with data obtained from IoT and non-IoT devices. The proposed framework comprises a self-designed deep CNN-LSTM along with ResNet50 and MobileNet transfer learning approaches. The CNN-LSTM architecture includes an enhanced squeeze and excitation block that improves overall performance.This architecture processes time series data transformed into a 3-Channel image structure via improved recurrence plot (RP), Gramian angular field (GAF), and fuzzy recurrence plot (FRP) methods. The proposed model demonstrated superior classification accuracy on the ECG5000 and TESS datasets compared to other state-of-the-art techniques, validating its efficacy for binary and multiclass classification.

show abstract

Investigation Using MLP-SVM-PCA Classifiers on Speech Emotion Recognition

Cited by 3 publications

References 21 publications

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

XEmoAccent: Embracing Diversity in Cross-Accent Emotion Recognition Using Deep Learning

Image Encoded Time Series Classification of Small Datasets: An Innovative Architecture Using Deep Learning Ensembles

Contact Info

Product

Resources

About