Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8513222 | EMBC |
Field | DocType | Volume |
Computer vision,Signal processing,Computer science,Spectrogram,Convolutional neural network,Normal voice,Speech recognition,Feature extraction,Test data,Artificial intelligence,Classifier (linguistics) | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huiyi Wu | 1 | 0 | 0.34 |
| John J. Soraghan | 2 | 166 | 34.16 |
| Anja Lowit | 3 | 18 | 2.62 |
| Gaetano Di Caterina | 4 | 11 | 5.32 |