  Contextual knowledge is important for real-world automatic speech recognition
(ASR) applications. In this paper, a novel tree-constrained pointer generator
(TCPGen) component is proposed that incorporates such knowledge as a list of
biasing words into both attention-based encoder-decoder and transducer
end-to-end ASR models in a neural-symbolic way. TCPGen structures the biasing
words into an efficient prefix tree to serve as its symbolic input and creates
a neural shortcut between the tree and the final ASR output distribution to
facilitate recognising biasing words during decoding. Systems were trained and
evaluated on the Librispeech corpus where biasing words were extracted at the
scales of an utterance, a chapter, or a book to simulate different application
scenarios. Experimental results showed that TCPGen consistently improved word
error rates (WERs) compared to the baselines, and in particular, achieved
significant WER reductions on the biasing words. TCPGen is highly efficient: it
can handle 5,000 biasing words and distractors and only add a small overhead to
memory use and computation cost.
