Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition

The choice of modeling units affects the performance of the acoustic modeling and plays an important role in automatic speech recognition (ASR). In mandarin scenarios, the Chinese characters represent meaning but are not directly related to the pronunciation. Thus only considering the writing of Chinese characters as modeling units is insufficient to capture speech features. In this paper, we present a novel method involves with multi-level modeling units, which integrates multi-level information for mandarin speech recognition. Specifically, the encoder block considers syllables as modeling units, and the decoder block deals with character modeling units. During inference, the input feature sequences are converted into syllable sequences by the encoder block and then converted into Chinese characters by the decoder block. This process is conducted by a unified end-to-end model without introducing additional conversion models. By introducing InterCE auxiliary task, our method achieves competitive results with CER of 4.1%/4.6% and 4.6%/5.2% on the widely used AISHELL-1 benchmark without a language model, using the Conformer and the Transformer backbones respectively.