Contextual biasing is an important and challenging task for end-to-end
automatic speech recognition (ASR) systems, which aims to achieve better
recognition performance by biasing the ASR system to particular context phrases
such as person names, music list, proper nouns, etc. Existing methods mainly
include contextual LM biasing and adding bias encoder into end-to-end ASR
models. In this work, we introduce a novel approach to do contextual biasing by
adding a contextual spelling correction model on top of the end-to-end ASR
system. We incorporate contextual information into a sequence-to-sequence
spelling correction model with a shared context encoder. Our proposed model
includes two different mechanisms: autoregressive (AR) and non-autoregressive
(NAR). We propose filtering algorithms to handle large-size context lists, and
performance balancing mechanisms to control the biasing degree of the model. We
demonstrate the proposed model is a general biasing solution which is
domain-insensitive and can be adopted in different scenarios. Experiments show
that the proposed method achieves as much as 51% relative word error rate (WER)
reduction over ASR system and outperforms traditional biasing methods. Compared
to the AR solution, the proposed NAR model reduces model size by 43.2% and
speeds up inference by 2.1 times.

本研究提出了一种新方法，通过在端到端自动语音识别系统的顶部加入上下文拼写纠错模型，将上下文信息纳入序列到序列拼写纠正模型中，从而提高了识别速度和准确性。

面向端到端语音识别系统的上下文拼写纠正定制

Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems

Attention-based sequence-to-sequence models for speech recognition jointly
train an acoustic model, language model (LM), and alignment mechanism using a
single neural network and require only parallel audio-text pairs. Thus, the
language model component of the end-to-end model is only trained on transcribed
audio-text pairs, which leads to performance degradation especially on rare
words. While there have been a variety of work that look at incorporating an
external LM trained on text-only data into the end-to-end framework, none of
them have taken into account the characteristic error distribution made by the
model. In this paper, we propose a novel approach to utilizing text-only data,
by training a spelling correction (SC) model to explicitly correct those
errors. On the LibriSpeech dataset, we demonstrate that the proposed model
results in an 18.6% relative improvement in WER over the baseline model when
directly correcting top ASR hypothesis, and a 29.0% relative improvement when
further rescoring an expanded n-best list using an external LM.

该研究提出了一种新方法以训练拼写纠错模型来纠正注意力机制序列到序列语音识别模型中的错误，从而改进了性能。在 LibriSpeech 数据集上，该模型相对于基线模型的相对改进为 18.6％，相对于使用扩展语言模型重新评分的 n-best 列表的改进为 29.0％。