2022
INTERSPEECH
INTERSPEECH 2022
Simple and Effective Zero-shot Cross-lingual Phoneme Recognition
Abstract
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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Speech & Audio
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Zero-Shot Learning
Speech & Audio > Recognition > Speech Recognition
Artificial Intelligence > Learning Paradigms > Zero-Shot Learning
Deep Learning > Learning Types > Transfer Learning