There is a newer version of this record available.

Dataset Open Access

Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)

Yougen Yuan; Cheung-Chi Leung; Lei Xie; Hongjie Chen; Bin Ma; Haizhou Li

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 (8.1 GB)
Name Size
10_5281_zenodo_809197.tar.gz
md5:d2e055d9f9cc861d4a9c3a8eb404a1b2
8.1 GB Download
37
0
views
downloads
All versions This version
Views 373
Downloads 00
Data volume 0 Bytes0 Bytes
Unique views 333
Unique downloads 00

Share

Cite as