Acoustic analysis using signal processing tools can be used to extract voice features to distinguish whether a voice is pathological or healthy. The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice. The novel classifier achieved 88.5%, 66.2% and 77.0% accuracy on training, validation and testing data set respectively on 482 normal and 482 organic dysphonia speech files. It reveals that the proposed novel algorithm on the Saarbruecken Voice Database can effectively been used for screening pathological voice recordings.
|Number of pages||4|
|Publication status||Published - 17 Jul 2018|
|Event||40th International Conference of the IEEE Engineering in Medicine and Biology Society - Honolulu, Hawaii, United States|
Duration: 17 Jul 2018 → 21 Jul 2018
|Conference||40th International Conference of the IEEE Engineering in Medicine and Biology Society|
|Abbreviated title||EMBC 2018|
|Period||17/07/18 → 21/07/18|
- acoustic analysis
- signal processing
- pathological voice detection
- healthy voice detection
Wu, H., Soraghan, J., Lowit, A., & Di Caterina, G. (2018). Convolutional neural networks for pathological voice detection. Paper presented at 40th International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Hawaii, United States.