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.

翻译：作为自动语音识别（ASR）和文本转语音（TTS）前端的关键组件，字素到音素（G2P）转换负责将字母转换为相应的发音。现有方法要么速度慢、性能差，要么在应用场景中受限，尤其在设备端推理过程中。本文融合了专家知识与基于连接时序分类（CTC）的神经网络的优势，提出了一种名为LiteG2P的新方法，该方法快速、轻量且理论上可并行化。通过精心设计，LiteG2P可同时应用于云端和设备端。在CMU数据集上的实验结果表明，所提方法的性能优于最先进的基于CTC的方法，且参数量减少10倍，甚至与最先进的基于Transformer的序列到序列模型性能相当，同时参数量更少，计算量减少33倍。