Published June 15, 2017
| Version v1
Dataset
Open
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)
Authors/Creators
- 1. Northwestern Polytechnical University
- 2. Institute for Infocomm Research
- 3. National University of Singapore
Description
The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.
Files
Files
(8.1 GB)
| Name | Size | |
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md5:d2e055d9f9cc861d4a9c3a8eb404a1b2
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8.1 GB | Download |