514 citations · 588 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
Szu-Wei Fu, Cheng Yu, Tsun-An Hsieh +4
The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory. Ob…
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-…