43 citations · 77 across the 6 of their papers we have counts for
7 papers
Investigation and Analysis of Hyper and Hypo neuron pruning to selectively update neurons during Unsupervised Adaptation
Vikramjit Mitra, Horacio Franco
Unseen or out-of-domain data can seriously degrade the performance of a neural network model, indicating the model's failure to generalize to unseen data. Neural net pruning can no…
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
Emre Yılmaz, Vikramjit Mitra, Ganesh Sivaraman +1
The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One promi…
Articulatory Features for ASR of Pathological Speech
Emre Yılmaz, Vikramjit Mitra, Chris Bartels +1
In this work, we investigate the joint use of articulatory and acoustic features for automatic speech recognition (ASR) of pathological speech. Despite long-lasting efforts to buil…
Voices Obscured in Complex Environmental Settings (VOICES) corpus
Colleen Richey, Maria A. Barrios, Zeb Armstrong +11
This paper introduces the Voices Obscured In Complex Environmental Settings (VOICES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote spe…
Interpreting DNN output layer activations: A strategy to cope with unseen data in speech recognition
Vikramjit Mitra, Horacio Franco
Unseen data can degrade performance of deep neural net acoustic models. To cope with unseen data, adaptation techniques are deployed. For unlabeled unseen data, one must generate s…
Articulatory information and Multiview Features for Large Vocabulary Continuous Speech Recognition
Vikramjit Mitra, Wen Wang, Chris Bartels +2
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 s…
Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition
Vikramjit Mitra, Horacio Franco
Unseen data conditions can inflict serious performance degradation on systems relying on supervised machine learning algorithms. Because data can often be unseen, and because tradi…