Deep neural networks for acoustic modeling – a review
| dc.contributor.author | Siriwardene, S | |
| dc.contributor.editor | Fernando, KSD | |
| dc.date.accessioned | 2022-11-29T08:08:13Z | |
| dc.date.available | 2022-11-29T08:08:13Z | |
| dc.date.issued | 2016-12 | |
| dc.description.abstract | Acoustic modeling refers to a statistical model that converts the speech signal to a set of phonetics related to each set of feature vectors extracted through pre processing the sound signal. A traditional approach to this problem is Hidden Markov Models (HMM), a probability model that maps each input with a hidden state. Deep neural networks are used for acoustic modeling due to their efficient feature extraction ability. This paper reviews the various forms of neural networks used in combination with HMMs for speech recognition. | en_US |
| dc.identifier.citation | ****** | en_US |
| dc.identifier.conference | International Conference on Information Technology Research 2016 | en_US |
| dc.identifier.department | Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. | en_US |
| dc.identifier.faculty | IT | en_US |
| dc.identifier.pgnos | pp. 45-51 | en_US |
| dc.identifier.place | Moratuwa. Sri Lanka | en_US |
| dc.identifier.proceeding | Proceedings of the International Conference in Information Technology Research 2016 | en_US |
| dc.identifier.uri | http://dl.lib.uom.lk/handle/123/19613 | |
| dc.identifier.year | 2016 | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka | en_US |
| dc.subject | Hidden markov model | en_US |
| dc.subject | Deep neural network | en_US |
| dc.subject | Deep belief networks | en_US |
| dc.subject | Convolutional neural networks | en_US |
| dc.title | Deep neural networks for acoustic modeling – a review | en_US |
| dc.type | Conference-Full-text | en_US |