  As a key component of automated speech recognition (ASR) and the front-end in
text-to-speech (TTS), grapheme-to-phoneme (G2P) plays the role of converting
letters to their corresponding pronunciations. Existing methods are either slow
or poor in performance, and are limited in application scenarios, particularly
in the process of on-device inference. In this paper, we integrate the
advantages of both expert knowledge and connectionist temporal classification
(CTC) based neural network and propose a novel method named LiteG2P which is
fast, light and theoretically parallel. With the carefully leading design,
LiteG2P can be applied both on cloud and on device. Experimental results on the
CMU dataset show that the performance of the proposed method is superior to the
state-of-the-art CTC based method with 10 times fewer parameters, and even
comparable to the state-of-the-art Transformer-based sequence-to-sequence model
with less parameters and 33 times less computation.
