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Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping
April 12, 2024, 4:42 a.m. | Kevin Zhang, Luka Chkhetiani, Francis McCann Ramirez, Yash Khare, Andrea Vanzo, Michael Liang, Sergio Ramirez Martin, Gabriel Oexle, Ruben Bousbib, Ta
cs.LG updates on arXiv.org arxiv.org
Abstract: This paper presents Conformer-1, an end-to-end Automatic Speech Recognition (ASR) model trained on an extensive dataset of 570k hours of speech audio data, 91% of which was acquired from publicly available sources. To achieve this, we perform Noisy Student Training after generating pseudo-labels for the unlabeled public data using a strong Conformer RNN-T baseline model. The addition of these pseudo-labeled data results in remarkable improvements in relative Word Error Rate (WER) by 11.5% and 24.3% …
abstract acquired arxiv asr audio automatic speech recognition bootstrapping cs.cl cs.lg cs.sd data dataset eess.as labels paper public public data recognition robust scale speech speech recognition training type via
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