| Paper ID | SPE-55.1 |
| Paper Title |
Unsupervised neural adaptation model based on optimal transport for spoken language identification |
| Authors |
Xugang Lu, Peng Shen, National Institute of Information and Communications Technology, Japan; Yu Tsao, Academic Sinica, Taiwan; Hisashi Kawai, National Institute of Information and Communications Technology, Japan |
| Session | SPE-55: Language Identification and Low Resource Speech Recognition |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-MULT] Multilingual Recognition and Identification |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Due to the mismatch of statistical distributions of acoustic speech between training and testing sets, the performance of spoken language identification (SLID) could be drastically degraded. In this paper, we propose an unsupervised neural adaptation model to deal with the distribution mismatch problem for SLID. In our model, we explicitly formulate the adaptation as to reduce the distribution discrepancy on both feature and classifier for training and testing data sets. Moreover, inspired by the strong power of the optimal transport (OT), a Wasserstein distance metric is designed as the adaptation loss to measure probability distribution discrepancy. By minimizing the classification loss on the training data set with the adaptation loss on both training and testing data sets, the statistical distribution difference between training and testing domains is reduced. We carried out SLID experiments on the oriental language recognition (OLR) challenge data corpus where the training and testing data sets were collected from different conditions. Our results showed that significant improvements were achieved on the cross domain test tasks. |
