3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.SD2024
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets
Jiatong Shi, Shih-Heng Wang, William Chen +8
ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of language identification and automatic speech recognition (ASR). This benchmark treats the models as featur…
cs.CL2023★ 3 cited
Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation
Martijn Bartelds, Nay San, Bradley McDonnell +2
The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech…
cs.CL2023★ 2 cited
Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions
Nay San, Martijn Bartelds, Blaine Billings +7
Recent research using pre-trained transformer models suggests that just 10 minutes of transcribed speech may be enough to fine-tune such a model for automatic speech recognition (A…