Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter

TL;DR

This paper introduces a cascade RNN-T model for Mandarin speech recognition that converts audio to syllables and then to characters, significantly improving recognition accuracy by leveraging extensive text data while maintaining low latency.

Contribution

It proposes a novel cascade RNN-T architecture that enhances language modeling in Mandarin ASR by combining syllable-to-character conversion, enabling better use of text data.

Findings

Outperforms character-based RNN-T on Mandarin test sets

Achieves higher recognition quality with similar latency

Effectively leverages large text repositories for language modeling

Abstract

End-to-end models are favored in automatic speech recognition (ASR) because of its simplified system structure and superior performance. Among these models, recurrent neural network transducer (RNN-T) has achieved significant progress in streaming on-device speech recognition because of its high-accuracy and low-latency. RNN-T adopts a prediction network to enhance language information, but its language modeling ability is limited because it still needs paired speech-text data to train. Further strengthening the language modeling ability through extra text data, such as shallow fusion with an external language model, only brings a small performance gain. In view of the fact that Mandarin Chinese is a character-based language and each character is pronounced as a tonal syllable, this paper proposes a novel cascade RNN-T approach to improve the language modeling ability of RNN-T. Our approach firstly uses an RNN-T to transform acoustic feature into syllable sequence, and then converts the syllable sequence into character sequence through an RNN-T-based syllable-to-character converter. Thus a rich text repository can be easily used to strengthen the language model ability. By introducing several important tricks, the cascade RNN-T approach surpasses the character-based RNN-T by a large margin on several Mandarin test sets, with much higher recognition quality and similar latency.