  Deep biasing for the Transducer can improve the recognition performance of
rare words or contextual entities, which is essential in practical
applications, especially for streaming Automatic Speech Recognition (ASR).
However, deep biasing with large-scale rare words remains challenging, as the
performance drops significantly when more distractors exist and there are words
with similar grapheme sequences in the bias list. In this paper, we combine the
phoneme and textual information of rare words in Transducers to distinguish
words with similar pronunciation or spelling. Moreover, the introduction of
training with text-only data containing more rare words benefits large-scale
deep biasing. The experiments on the LibriSpeech corpus demonstrate that the
proposed method achieves state-of-the-art performance on rare word error rate
for different scales and levels of bias lists.
