Name
Abstract | ||
|---|---|---|
Dysarthria is a motor speech disorder, characterized by slurred or slow speech resulting in low intelligibility. Automatic recognition of dysarthric speech is beneficial to enable people with dysarthria to use speech as a mode of interaction with electronic devices. In this paper we propose a mechanism to adapt the tempo of sonorant part of dysarthric speech to match that of normal speech, based on the severity of dysarthria. We show a significant improvement in recognition of tempo-adapted dysasrthic speech, using a Gaussian Mixture Model (GMM) Hidden Markov Model (HMM) recognition system as well as a Deep neural network (DNN) - HMM based system. All evaluations were done on Universal Access Speech Corpus. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-43958-7_44 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Dysarthria,Tempo adaptation,Disordered speech,Speech recognition | Speech corpus,Computer science,Speech recognition,Motor speech disorders,Artificial neural network,Sonorant,Hidden Markov model,Dysarthria,Mixture model,Intelligibility (communication) | Conference |
Volume | ISSN | Citations |
9811 | 0302-9743 | 1 |
PageRank | References | Authors |
0.39 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chitralekha Bhat | 1 | 2 | 3.13 |
| Bhavik B. Vachhani | 2 | 22 | 4.69 |
| Sunil Kumar Kopparapu | 3 | 42 | 25.18 |