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In the second approach, the DNN is used to extract bottleneck features that are then used as inputs for a state-of-the-art i-vector system.
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Finally, the bottleneck features extracted from the bottleneck layer of the DNN were used to train the speaker model.
The large vocabulary continuous speech recognition experiment results on the Switchboard database indicate that the algorithm proposed in this paper can extract the speech bottleneck features with priori information, and reduce the word error rate in continuous speech recognition.
Second, the speech bottleneck features can be extracted from the bottleneck layer of the sparse bottleneck DNN (BN-DNN).
This method extracts the speech bottleneck features which contain supervised class information and adjacent voice frames related information by changing the architecture of deep neural network (DNN), to improve of the performance speech recognition.
Effective means to recover microalgae and extract their intracellular lipids remains a practical and economic bottleneck in algal biodiesel production.
This paper presents FlowWalker, a novel dynamic taint analysis framework that aims to extract the complete taint data flow while eliminating the bottlenecks that occur in existing tools, with applications to file-format reverse engineering.
Since this approach relies on trained expert biocurators who read and extract 'curatable' information from the published literature, curation can become a potential bottleneck, both in terms of speed and cost.
Bottleneck features extracted by a multi-layer perceptron (MLP) can be used for nonlinear feature transformation [20].
Bottleneck features extracted by a multilayer perceptron (MLP) can be used for nonlinear feature transformation and dimensionality reduction [35].
Therefore, the likelihood of the bottleneck features extracted from the DNN and that of cepstral domain DAE-based dereverberation are combined linearly.
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