Composite Embedding Systems Based on DNN-HMM and Attention End-To-End for ZeroSpeech2017 track1 (2)
收藏NIAID Data Ecosystem2026-03-11 收录
下载链接：
https://zenodo.org/record/823694
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资源简介：
Deep neural networks (DNNs) were trained for posterior and bottleneck features using Japanese and other language speech data. We explore various DNN types, their combinations, and dimension reduction by principal component analysis (PCA).
This version (version 2 ) concatenates CSJ feature vector and PCA compressed feature vector made from attention end-to-end feature.
X:CSJ feature (60 dim bottleneck, (version 1 feature))
S:Attention end-to-end feature (320 dim)
T:PCA(S) (60 dim)
Z=concat(X,T)
创建时间：
2020-01-21
