Multi-Task Learning for Speech Recognition: An Overview
نویسندگان
چکیده
Generalization is a common issue for automatic speech recognition. A successful method used to improve recognition results consists of training a single system to solve multiple related tasks in parallel. This overview investigates which auxiliary tasks are helpful for speech recognition when multi-task learning is applied on a deep learning based acoustic model. The impact of multi-task learning on speech recognition related tasks, such as speaker adaptation, or robustness to noise, is also examined.
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