22 citations · 28 across the 7 of their papers we have counts for
4 papers · 1 filter
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision
Chak-Fai Li, Francis Keith, William Hartmann +1
Advances in self-supervised learning have significantly reduced the amount of transcribed audio required for training. However, the majority of work in this area is focused on read…
Combining Unsupervised and Text Augmented Semi-Supervised Learning for Low Resourced Autoregressive Speech Recognition
Chak-Fai Li, Francis Keith, William Hartmann +1
Recent advances in unsupervised representation learning have demonstrated the impact of pretraining on large amounts of read speech. We adapt these techniques for domain adaptation…
Overcoming Domain Mismatch in Low Resource Sequence-to-Sequence ASR Models using Hybrid Generated Pseudotranscripts
Chak-Fai Li, Francis Keith, William Hartmann +2
Sequence-to-sequence (seq2seq) models are competitive with hybrid models for automatic speech recognition (ASR) tasks when large amounts of training data are available. However, da…
Using heterogeneity in semi-supervised transcription hypotheses to improve code-switched speech recognition
Andrew Slottje, Shannon Wotherspoon, William Hartmann +2
Modeling code-switched speech is an important problem in automatic speech recognition (ASR). Labeled code-switched data are rare, so monolingual data are often used to model code-s…