Optimal Feature Learning for Speech Emotion Recognition – A DeepNet Approach

Bhushan, Bharat

doi:10.1109/icdsns58469.2023.10245147

Cited by 1 publication

(1 citation statement)

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“…Emotions are considered an integral and important part of human life, and they are a way to express one’s opinions and inform others about one’s physical and mental health [ 1 ]. Speech signals have emerged as a valuable source of information about a speaker’s emotions, and they have the advantage of easy recording compared to other physiological signals that require special equipment [ 2 ].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%