Biologically-Inspired Speech Emotion Recognition Using Rate Map Representations: An Application to the ShEMO Persian Speech Database
DOI:
https://doi.org/10.5281/zenodo.10396163Anahtar Kelimeler:
Speech Emotion Recognition- Rate Map- Machine LearningÖz
This paper presents an innovative Speech Emotion Recognition (SER) model, inspired by the human auditory system, for analyzing and interpreting emotions in speech. Our proposed model utilizes a rate map representation to encode the spectro-temporal characteristics of auditory nerve activity, closely mimicking the intricate processes of human auditory perception. This model comprises several stages: pre-emphasis of the audio signal, cochlear filtering using a Gammatone Filter bank (GTF), neuromechanical transduction modeled by the Dau inner hair cell model, and the assembly of a rate map representation through integration of responses from each frequency channel. We apply this model to the ShEMO database, an extensive collection of Persian emotional speech, to detect and classify a spectrum of emotions. Our experimental results, obtained using deep learning architectures, demonstrate the effectiveness of the proposed model. We report the highest classification metrics with the Mobilenet architecture, achieving a performance of 71.57% and an F1 score of 51.52%. Overall, this study contributes to the field of speech emotion detection by offering a biologically-inspired model, validated with a substantial dataset, and yielding promising results in emotion classification using advanced machine learning techniques.
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