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Uncertainty estimation of DNN classifiers

2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), 2015
New efficient measures for estimating uncertainty of deep neural network (DNN) classifiers are proposed and successfully applied to multistream-based unsupervised adaptation of ASR systems to address uncertainty derived from noise. The proposed measure is the error from associative memory models trained on outputs of a DNN.
Sri Harish Reddy Mallidi, Tetsuji Ogawa, Hynek Hermansky +2 more
openaire +1 more source

Implementation of DNNs on IoT devices

Neural Computing and Applications, 2019
Driven by the recent growth in the fields of internet of things (IoT) and deep neural networks (DNNs), DNN-powered IoT devices are expected to transform a variety of industrial applications. DNNs, however, involve many parameters and operations to process the data generated by IoT devices.
Zhichao Zhang, Abbas Z. Kouzani
openaire +1 more source

DNN-based laughter synthesis

2019 International Conference on Control, Automation and Diagnosis (ICCAD), 2019
Just like speech is part of our daily life, non verbal sounds are the same. Until nowadays speech has been taken into consideration by the most of researchers, whose based their research work on analyzing, recognizing, synthesizing and even translating it.
Nadia Mansouri, Zied Lachiri
openaire +1 more source

Increasing Throughput of In-Memory DNN Accelerators by Flexible Layerwise DNN Approximation