  End-to-end TTS requires a large amount of speech/text paired data to cover
all necessary knowledge, particularly how to pronounce different words in
diverse contexts, so that a neural model may learn such knowledge accordingly.
But in real applications, such high demand of training data is hard to be
satisfied and additional knowledge often needs to be injected manually. For
example, to capture pronunciation knowledge on languages without regular
orthography, a complicated grapheme-to-phoneme pipeline needs to be built based
on a large structured pronunciation lexicon, leading to extra, sometimes high,
costs to extend neural TTS to such languages. In this paper, we propose a
framework to learn to automatically extract knowledge from unstructured
external resources using a novel Token2Knowledge attention module. The
framework is applied to build a TTS model named Neural Lexicon Reader that
extracts pronunciations from raw lexicon texts in an end-to-end manner.
Experiments show the proposed model significantly reduces pronunciation errors
in low-resource, end-to-end Chinese TTS, and the lexicon-reading capability can
be transferred to other languages with a smaller amount of data.
