Simulation of English speech emotion recognition based on transfer learning and CNN neural network

Issue title: Complex evolutionary artificial intelligence in cognitive digital twinning

Guest editors: Neal Wagner, Sundhararajan, Le Hoang Son and Meng Joo

Article type: Research Article

Authors: Chen, Xuehua^{; *}

Affiliations: Hainan College of Foreign Studies, Wenchang, China

Correspondence: [*] Corresponding author. Xuehua Chen, Hainan College of Foreign Studies, Wenchang, China. E-mail: [email protected].

Abstract: The difference between English and Chinese expressions is that English emphasizes the stress of syllables, so the recognition of English speech emotions plays an important role in learning English. This study uses transfer learning as the technical support to study English speech emotion recognition. The acoustic model based on weight transfer has two different training strategies: single-stage training and two-stage training strategy. By comparing the performance of the English speech emotion recognition model based on CNN neural network and the model proposed in this paper, the statistical comparison data is drawn into a statistical graph. The research results show that transfer learning has certain advantages over other algorithms in English speech emotion recognition. In the subsequent teaching and real-time translation equipment research, transfer learning can be applied to English models.

Keywords: Transfer learning, English, speech, emotion recognition, features

DOI: 10.3233/JIFS-189231

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2349-2360, 2021