3 citations · 3 across the 3 of their papers we have counts for
3 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…