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Found 6 papers, 0 papers with code
Investigation and Analysis of Hyper and Hypo neuron pruning to selectively update neurons during Unsupervised Adaptation
This work investigates if pruning approaches are successful in detecting neurons that are either high-salient (mostly active or hyper) or low-salient (barely active or hypo), and whether removal of such neurons can help to improve the model's generalization capacity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Articulatory Features for ASR of Pathological Speech
In this work, we investigate the joint use of articulatory and acoustic features for automatic speech recognition (ASR) of pathological speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Articulatory information and Multiview Features for Large Vocabulary Continuous Speech Recognition
This paper explores the use of multi-view features and their discriminative transforms in a convolutional deep neural network (CNN) architecture for a continuous large vocabulary speech recognition task.
Interpreting DNN output layer activations: A strategy to cope with unseen data in speech recognition
This work proposes a strategy to assess a model's performance by analyzing the output layer activations by using a distance measure between the most likely target and the next most likely target, which is used for data selection for performing unsupervised adaptation.
Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition
This work aims to estimate the propagation of such distortion in the form of network activation entropy, which is measured over a short- time running window on the activation from each neuron of a given hidden layer, and these measurements are then used to compute summary entropy.
