Emotional speaker identification using a novel capsule nets model
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Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Speaker recognition systems are widely used in various applications to identify a person by their voice; however, the high degree of variability in speech signals makes this a challenging task. Dealing with emotional variations is very difficult because emotions alter the voice characteristics of a person; thus, the acoustic features differ from those used to train models in a neutral environment. Therefore, speaker recognition models trained on neutral speech fail to correctly identify speakers under emotional stress. Although considerable advancements in speaker identification have been made using convolutional neural networks (CNN), CNNs cannot exploit the spatial association between low-level features. Inspired by the recent introduction of capsule networks (CapsNets), which are based on deep learning to overcome the inadequacy of CNNs in preserving the pose relationship between low-level features with their pooling technique, this study investigates the performance of using CapsNets in identifying speakers from emotional speech recordings. A CapsNet-based speaker identification model is proposed and evaluated using three distinct speech databases, i.e., the Emirati Speech Database, SUSAS Dataset, and RAVDESS (open-access). The proposed model is also compared to baseline systems. Experimental results demonstrate that the novel proposed CapsNet model trains faster and provides better results over current stateof-the-art schemes. The effect of the routing algorithm on speaker identification performance was also studied by varying the number of iterations, both with and without a decoder network.
Açıklama
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors would also like to thank the University of Sharjah for funding this project.
Anahtar Kelimeler
Capsule Network, Convolutional Neural Network, Emotional Speech, Recognition, Computer, System
Kaynak
Expert Systems with Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
193
Sayı
Künye
Nassif, A. B., Shahin, I., Elnagar, A., Velayudhan, D., Alhudhaif, A., & Polat, K. (2022). Emotional speaker identification using a novel capsule nets model. Expert Systems with Applications, 193, 116469.
