Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization

Bandela, Surekha Reddy; Siva Priyanka, S.; Sunil Kumar, K.; Vijay Bhaskar Reddy, Y.; Berhanu, Afework Aemro

doi:10.1155/2023/5765760

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Bandela et al proposed a novel feature extraction method using the teager energy operator (TEO) to detect stress emotions. TEO was specifically designed to increase the energy of accented speech signals [ 31 ]. For gender-dependent and speaker-independent conditions, a KNN classifier was used for emotion classification in EMA (English), EMOVO (Italian), EMO-DB (German), and IITKGP (Telugu) databases.…”

Section: Related Workmentioning

confidence: 99%

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Yue,

Hu,

Zhu

2024

Biomimetics

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Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.

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Section: Related Workmentioning

confidence: 99%

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Yue,

Hu,

Zhu

2024

Biomimetics

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Graph-Based Multi-Feature Fusion Method for Speech Emotion Recognition

Liu,

Lin,

Wang

2024

Int. J. Patt. Recogn. Artif. Intell.

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Exploring proper way to conduct multi-speech feature fusion for cross-corpus speech emotion recognition is crucial as different audio features could provide complementary cues reflecting human emotion status. Speech emotion recognition allows computers to analyze the specific emotional condition of the speaker through speech, which is of great significance to the development of human–computer interaction technology. While most previous approaches only extract a single speech feature for emotion recognition, existing fusion methods such as concatenation, parallel connection, and splicing ignore heterogeneous patterns in the interaction between features and features, resulting in performance of existing systems. In this paper, we propose a novel graph-based fusion method to explicitly model the relationships between every pair of audio features, which provides a new research idea for speech feature fusion. Specifically, we propose a multi-dimensional edge features learning strategy called graph-based multi-feature fusion method for speech emotion recognition. It represents each speech feature as a node and learns multi-dimensional edge features to explicitly describe the relationship between each feature-feature pair in the context of emotion recognition. This way, the learned multi-dimensional edge features encode speech feature-level information from both the vertex and edge dimensions. Our approach consists of three modules: an Audio Feature Generation (AFG) module, an Audio-Feature Multi-dimensional Edge Feature (AMEF) module and a Speech Emotion Recognition (SER) module. The proposed methodology yielded satisfactory outcomes on the SEWA dataset. Furthermore, the method demonstrated enhanced performance compared to the baseline in the AVEC 2019 Workshop and Challenge. We used data from two cultures as our training and validation sets: two cultures containing German and Hungarian on the SEWA dataset, the CCC scores for German are improved by 17.28% for arousal and 7.93% for liking, and for Hungarian, the CCC scores are improved by 11.15% for arousal and 131.11% for valence. The outcomes of our methodology demonstrate a 13% improvement over alternative fusion techniques, including those employing one-dimensional edge-based feature fusion approach. The experiments on some parts of the Aff-Wild 2 dataset demonstrate that our approach exhibits a certain degree of generalizability and robustness. Code is available at https://github.com/ChaosWang666/Graph-based-multi-Feature-fusion-method .

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Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization

Cited by 2 publications

References 39 publications

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Graph-Based Multi-Feature Fusion Method for Speech Emotion Recognition

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