  For Mandarin end-to-end (E2E) automatic speech recognition (ASR) tasks,
compared to character-based modeling units, pronunciation-based modeling units
could improve the sharing of modeling units in model training but meet
homophone problems. In this study, we propose to use a novel
pronunciation-aware unique character encoding for building E2E RNN-T-based
Mandarin ASR systems. The proposed encoding is a combination of
pronunciation-base syllable and character index (CI). By introducing the CI,
the RNN-T model can overcome the homophone problem while utilizing the
pronunciation information for extracting modeling units. With the proposed
encoding, the model outputs can be converted into the final recognition result
through a one-to-one mapping. We conducted experiments on Aishell and MagicData
datasets, and the experimental results showed the effectiveness of the proposed
method.

Computation and Language;Sound;Audio and Speech Processing