Objective The identification of TCM constitution plays an important role in"treating and preventing diseases"of TCM.At present,the identification of damp-heat constitution and balanced constitution is mostly determined by questionnaire,and subjective factors have a great influence.Aiming at the identification of damp-heat constitution and balanced constitution in TCM,this paper utilizes voice signal to automatically realize the constitution identification task,in order to provide assistance for the clinical identification of TCM constitution.Methods Based on deep learning Transformer and transfer learning,a pure attentional mechanism model was designed for the identification of constitution in TCM sound diagnosis.We collected 700 voices from 34 subjects,pre-processed the voice data to obtain the corresponding Mayer spectrum diagram,and used the Transformer model pre-trained based on the public data set to improve the performance of the model for audio classification.Results The accuracy of the experimental results was 83.33%,the AUC was 92.16%,the sensitivity was 80.25%,and the specificity was 87.03%.Compared with the Convolutional Neural Network(CNN),the performance of the deep learning model was better.Conclusion In this paper,the damp-heat constitution and balanced constitution identification model Transformer has achieved better identification effect,indicating that it can improve the efficiency of TCM acoustic diagnosis of constitution identification,and promote the objective and intelligent development of constitution identification.

Objective The identification of TCM constitution plays an important role in"treating and preventing diseases"of TCM.At present,the identification of damp-heat constitution and balanced constitution is mostly determined by questionnaire,and subjective factors have a great influence.Aiming at the identification of damp-heat constitution and balanced constitution in TCM,this paper utilizes voice signal to automatically realize the constitution identification task,in order to provide assistance for the clinical identification of TCM constitution.Methods Based on deep learning Transformer and transfer learning,a pure attentional mechanism model was designed for the identification of constitution in TCM sound diagnosis.We collected 700 voices from 34 subjects,pre-processed the voice data to obtain the corresponding Mayer spectrum diagram,and used the Transformer model pre-trained based on the public data set to improve the performance of the model for audio classification.Results The accuracy of the experimental results was 83.33%,the AUC was 92.16%,the sensitivity was 80.25%,and the specificity was 87.03%.Compared with the Convolutional Neural Network(CNN),the performance of the deep learning model was better.Conclusion In this paper,the damp-heat constitution and balanced constitution identification model Transformer has achieved better identification effect,indicating that it can improve the efficiency of TCM acoustic diagnosis of constitution identification,and promote the objective and intelligent development of constitution identification.