2023
INTERSPEECH
INTERSPEECH 2023
Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages
Abstract
Recent models such as XLS-R and Whisper have made multilingual speech technologies more accessible by pre-training on audio from around 100 spoken languages each. However, there are thousands of spoken languages worldwide, and adapting to new languages is an important problem. In this work, we aim to understand which model adapts better to languages unseen during pre-training. We fine-tune both models on 13 unseen languages and 18 seen languages. Our results show that the number of hours seen per language and language family during pre-training is predictive of how the models compare, despite the significant differences in the pre-training methods.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing and Speech & Audio
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Hot Topic Early Bird
— multilingual speech
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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 > Weakly Supervised Learning
Natural Language Processing > Resources & Methods > Multilingual NLP
Speech & Audio > Recognition > Automatic Speech Recognition
Machine Learning > Learning Types > Transfer Learning
Machine Learning > Learning Paradigms > Self-Supervised Learning