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An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

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An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

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Data Synthesization for Classification in Autonomous Robotic Grasping System Using ‘Catalogue’-Style Images

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Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition

DNN based speaker embedding using content information for text-dependent speaker verification

Word Sequence Modeling using Deep Learning

Learning to Find Good Correspondences

Redundant Hash Addressing for Large-Scale Query by Example Spoken Query Detection

Fast Language Adaptation Using Phonological Information

Learning to Find Good Correspondences

Data Synthesization for Classification in Autonomous Robotic Grasping System Using ‘Catalogue’-Style Images

Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition

Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models

Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition