G2PU: Grapheme-To-Phoneme Transducer with Speech Units

Abstract

Most phoneme transcripts are generated using forced alignment: typically a grapheme-to-phoneme transducer (G2P) is applied to text sequences to generate candidate phoneme transcripts, which are then time-aligned to the waveform using an acoustic model. This paper demonstrates, for the first time, simultaneous optimization of the G2P, the acoustic model, and the acoustic alignment to a corpus. To this end, we propose G2PU, a joint CTC-attention model consisting of an encoder-decoder G2P network and an encoder-CTC unit-to-phoneme (U2P) network, where the units are extracted from speech. We demonstrate that the G2P and U2P, operating in parallel, produce lower phone error rates than those of state-of-the-art open-source G2P and forced alignment systems. Furthermore, although the G2P and U2P are trained using parallel speech and text, their synergy can be generalized to text-only test corpora if we also train a grapheme-to-unit (G2U) network that generates speech units from text in the absence of parallel speech. Our G2PU model is trained using phoneme transcripts generated by a teacher G2P tool. Our experiments on Chinese and Japanese show that G2PU reduces phoneme error rate by 7% to 29% relative compared to its teacher. Finally, we include case studies to provide insights into the system’s workings.