514 citations · 588 across the 11 of their papers we have counts for
12 papers
Comparison of Speech Tasks in Human Expert and Machine Detection of Parkinson's Disease
Peter Plantinga, Roozbeh Sattari, Karine Marcotte +8
The speech of people with Parkinson's Disease (PD) has been shown to hold important clues about the presence and progression of the disease. We investigate the factors based on whi…
From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease
Peter Plantinga, Jen-Kai Chen, Roozbeh Sattari +2
Speech holds promise as a cost-effective and non-invasive biomarker for neurological conditions such as Parkinson's disease (PD). While deep learning systems trained on raw audio c…
Open-Source Conversational AI with SpeechBrain 1.0
Mirco Ravanelli, Titouan Parcollet, Adel Moumen +30
SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly on speech processing tasks such as speech recognition, speech enhancement, speaker re…
Continual Learning for End-to-End ASR by Averaging Domain Experts
Peter Plantinga, Jaekwon Yoo, Chandra Dhir
Continual learning for end-to-end automatic speech recognition has to contend with a number of difficulties. Fine-tuning strategies tend to lose performance on data already seen, a…
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…
SpeechBrain: A General-Purpose Speech Toolkit
Mirco Ravanelli, Titouan Parcollet, Peter Plantinga +18
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech processing technologies by being simple, fle…
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…
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…
Towards Real-time Mispronunciation Detection in Kids' Speech
Peter Plantinga, Eric Fosler-Lussier
Modern mispronunciation detection and diagnosis systems have seen significant gains in accuracy due to the introduction of deep learning. However, these systems have not been evalu…
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-…
Electron Drift Directions in Strong-Field Double Ionization of Atoms
S. L. Haan, Z. S. Smith, K. N. Shomsky +1
Longitudinal momentum spectra and electron drift directions are considered for several laser wavelengths in Non-Sequential Double Ionization of helium using three dimensional class…