Token-Level Contextual Network with Ladder-Shaped Attention for End-to-End ASR

Abstract

Contextual automatic speech recognition (ASR) plays an increasingly important role in addressing the long-tail issues of general ASR. In the past, contextual ASR mainly focused on phrase-level discussions, providing a convenient way to handle biasing phrases. This paper introduces a new contextual network for extracting context tokens, with a focus on optimizing contextual knowledge using token-level information. We enhance the fusion of context information and ASR acoustic features, recognizing that textual knowledge inherently represents a distinct modality compared to acoustic information. More importantly, we propose a creative approach to address the challenge of the increasing size of expanding token list compared to phrase list. Our method is tested on the LibriSpeech and AISHELL-2 datasets, the results demonstrate a 31.0%/23.3% Word Error Rate (WER) reduction on LibriSpeech and a 26.9% reduction Character Error Rate (CER) on the named entity (NE) set from AISHELL-2.