3 citations · 6 across the 6 of their papers we have counts for
6 papers
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers
Prashant Serai, Peidong Wang, Eric Fosler-Lussier
Modeling the errors of a speech recognizer can help simulate errorful recognized speech data from plain text, which has proven useful for tasks like discriminative language modelin…
Ultra-lightweight Neural Differential DSP Vocoder For High Quality Speech Synthesis
Prabhav Agrawal, Thilo Koehler, Zhiping Xiu +2
Neural vocoders model the raw audio waveform and synthesize high-quality audio, but even the highly efficient ones, like MB-MelGAN and LPCNet, fail to run real-time on a low-end de…
Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition
Philipp Klumpp, Pooja Chitkara, Leda Sarı +5
The awareness for biased ASR datasets or models has increased notably in recent years. Even for English, despite a vast amount of available training data, systems perform worse for…
Voice-preserving Zero-shot Multiple Accent Conversion
Mumin Jin, Prashant Serai, Jilong Wu +3
Most people who have tried to learn a foreign language would have experienced difficulties understanding or speaking with a native speaker's accent. For native speakers, understand…
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data
Vishal Sunder, Prashant Serai, Eric Fosler-Lussier
A Virtual Patient (VP) is a powerful tool for training medical students to take patient histories, where responding to a diverse set of spoken questions is essential to simulate na…
Hallucination of speech recognition errors with sequence to sequence learning
Prashant Serai, Vishal Sunder, Eric Fosler-Lussier
Automatic Speech Recognition (ASR) is an imperfect process that results in certain mismatches in ASR output text when compared to plain written text or transcriptions. When plain t…