Few-shot learning with attention-based sequence-to-sequence models

Understand

End-to-end approaches have recently become popular as a means of simplifying the training and deployment of speech recognition systems.

• However, they often require large amounts of data to perform well on large vocabulary tasks.
• With the aim of making end-to-end approaches usable by a broader range of researchers, we explore the potential to use end-to-end methods in small vocabulary contexts where smaller datasets may be used.
• A significant drawback of small-vocabulary systems is the difficulty of expanding the vocabulary beyond the original training samples -- therefore we also study strategies to extend the vocabulary with only few examples per new class (few-shot learning).