8 citations · 12 across the 5 of their papers we have counts for
6 papers
Low-resource Low-footprint Wake-word Detection using Knowledge Distillation
Arindam Ghosh, Mark Fuhs, Deblin Bagchi +2
As virtual assistants have become more diverse and specialized, so has the demand for application or brand-specific wake words. However, the wake-word-specific datasets typically u…
Perceptual Loss with Recognition Model for Single-Channel Enhancement and Robust ASR
Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier
Single-channel speech enhancement approaches do not always improve automatic recognition rates in the presence of noise, because they can introduce distortions unhelpful for recogn…
Speech Synthesis as Augmentation for Low-Resource ASR
Deblin Bagchi, Shannon Wotherspoon, Zhuolin Jiang +1
Speech synthesis might hold the key to low-resource speech recognition. Data augmentation techniques have become an essential part of modern speech recognition training. Yet, they…
Phonetic Feedback for Speech Enhancement With and Without Parallel Speech Data
Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier
While deep learning systems have gained significant ground in speech enhancement research, these systems have yet to make use of the full potential of deep learning systems to prov…
An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping
Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier
Spectral mapping uses a deep neural network (DNN) to map directly from noisy speech to clean speech. Our previous study found that the performance of spectral mapping improves grea…
Spectral feature mapping with mimic loss for robust speech recognition
Deblin Bagchi, Peter Plantinga, Adam Stiff +1
For the task of speech enhancement, local learning objectives are agnostic to phonetic structures helpful for speech recognition. We propose to add a global criterion to ensure de-…