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What is Acoustic Model Training For Non-Audible Murmur Rec about?

This paper presents a novel approach to training acoustic models for non-audible murmur (NAM) recognition by transforming normal speech data into NAM data. The proposed method enhances the accuracy of NAM recognition by utilizing existing normal speech databases and applying modified speaker adaptive training techniques. Experimental results indicate an approximate 2% increase in word accuracy compared to conventional methods, demonstrating the effectiveness of the approach in improving NAM recognition syst

Author

amhar05a

Language

EN