Name
Title | ||
|---|---|---|
HypernasalityNet: Deep recurrent neural network for automatic hypernasality detection |
Abstract | ||
|---|---|---|
The results show that LSTM-DRNN has robust feature mining ability and classification ability. This is the first work that applies the LSTM-DRNN technique to automatically detect hypernasality in cleft palate speech. The experimental results demonstrate the potential of deep learning on pathologist speech detection. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ijmedinf.2019.05.023 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Cleft palate speech,Hypernasal speech,Long short-term memory,Deep recurrent neural network,Feature mining | Data mining,Softmax function,Voice activity detection,Recurrent neural network,Speech recognition,Artificial intelligence,Deep learning,Medicine,Hypernasal speech,Mandarin Chinese,Medical diagnosis,Vocal tract | Journal |
Volume | ISSN | Citations |
129 | 1386-5056 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |