Computer Science > Artificial Intelligence
[Submitted on 8 Mar 2024 (v1), last revised 30 Apr 2024 (this version, v3)]
Title:Medical Speech Symptoms Classification via Disentangled Representation
View PDFAbstract:Intent is defined for understanding spoken language in existing works. Both textual features and acoustic features involved in medical speech contain intent, which is important for symptomatic diagnosis. In this paper, we propose a medical speech classification model named DRSC that automatically learns to disentangle intent and content representations from textual-acoustic data for classification. The intent representations of the text domain and the Mel-spectrogram domain are extracted via intent encoders, and then the reconstructed text feature and the Mel-spectrogram feature are obtained through two exchanges. After combining the intent from two domains into a joint representation, the integrated intent representation is fed into a decision layer for classification. Experimental results show that our model obtains an average accuracy rate of 95% in detecting 25 different medical symptoms.
Submission history
From: Pengcheng Li [view email][v1] Fri, 8 Mar 2024 02:42:34 UTC (1,401 KB)
[v2] Tue, 26 Mar 2024 01:51:37 UTC (1,402 KB)
[v3] Tue, 30 Apr 2024 01:47:37 UTC (1,414 KB)
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