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3 results for “DNN-HMM”
Composite Embedding Systems Based on DNN-HMM and Attention End-To-End for ZeroSpeech2017 track1 (1)
<p>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).</p> <p>This version (version 1) extracts DNN bottleneck features obtained from GMM based SAT features. The DNN and GMM were trained by speech data from the corpus of spontaneous Japanese (CSJ).</p>
Composite Embedding Systems Based on DNN-HMM and Attention End-To-End for ZeroSpeech2017 track1 (2)
<p>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).</p> <p>This version (version 2 ) concatenates CSJ feature vector and PCA compressed feature vector made from attention end-to-end feature.</p> <p>X:CSJ feature (60 dim bottleneck, (version 1 feature))</p> <p>S:Attention end-to-end feature (320 dim)</p> <p>T:PCA(S) (60 dim)</p> <p>Z=concat(X,T)</p>
Identification of Replication Timing Domains Using DNN-HMM
GEO Series GSE53984. Homo sapiens. 0 samples. Type: Third-party reanalysis; Other.
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