2021/09/23 by Qiantong Xu, Xu, Qiantong, Alexei Baevski +3 · 29 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and dialogue systems #cs.CL #cs.LG #cs.SD
paper · pdf · doi:10.48550/arxiv.2109.11680
arxiv created 2021/09/23 · openalex publication_date 2021/09/23 · arxiv updated 2021/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent progress in self-training, self-supervised pretraining and unsupervised learning enabled well performing speech recognition systems without any labeled data. However, in many cases there is labeled data available for related languages which is not utilized by these methods. This paper extends previous work on zero-shot cross-lingual transfer learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by mapping phonemes of the training languages to the target language using articulatory features. Experiments show that this simple method significantly outperforms prior work which introduced task-specific architectures and used only part of a monolingually pretrained model.