Contextual RNN-T For Open Domain ASR

Understand

End-to-end (E2E) systems for automatic speech recognition (ASR), such as RNN Transducer (RNN-T) and Listen-Attend-Spell (LAS) blend the individual components of a traditional hybrid ASR system - acoustic model, language model, pronunciation model - into a single neural network.

• While this has some nice advantages, it limits the system to be trained using only paired audio and text.
• Because of this, E2E models tend to have difficulties with correctly recognizing rare words that are not frequently seen during training, such as entity names.
• In this paper, we propose modifications to the RNN-T model that allow the model to utilize additional metadata text with the objective of improving performance on these named entity words.