Although contextualized automatic speech recognition (ASR) systems are
commonly used to improve the recognition of uncommon words, their effectiveness
is hindered by the inherent limitations of speech-text data availability. To
address this challenge, our study proposes to leverage extensive text-only
datasets and contextualize pre-trained ASR models using a straightforward
text-augmentation (TA) technique, all while keeping computational costs
minimal. In particular, to contextualize a pre-trained CIF-based ASR, we
construct a codebook using limited speech-text data. By utilizing a simple
codebook lookup process, we convert available text-only data into latent text
embeddings. These embeddings then enhance the inputs for the contextualized
ASR. Our experiments on diverse Mandarin test sets demonstrate that our TA
approach significantly boosts recognition performance. The top-performing
system shows relative CER improvements of up to 30% on rare words and 15%
across all words in general.

使用简单的文本增强技术借助大量纯文本数据集来构建编码簿，可以提高预训练的 ASR 模型的上下文信息，从而显著提升识别性能。

一种用于上下文化普通话语音识别的高效文本增强方法

An efficient text augmentation approach for contextualized Mandarin  speech recognition

Accurate recognition of rare and new words remains a pressing problem for
contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing
methods involve modification of the ASR model or the beam-search decoding
algorithm, complicating model reuse and slowing down inference. This work
presents a new approach to fast context-biasing with CTC-based Word Spotter
(CTC-WS) for CTC and Transducer (RNN-T) ASR models. The proposed method matches
CTC log-probabilities against a compact context graph to detect potential
context-biasing candidates. The valid candidates then replace their greedy
recognition counterparts in corresponding frame intervals. A Hybrid
Transducer-CTC model enables the CTC-WS application for the Transducer model.
The results demonstrate a significant acceleration of the context-biasing
recognition with a simultaneous improvement in F-score and WER compared to
baseline methods. The proposed method is publicly available in the NVIDIA NeMo
toolkit.

通过 CTC-based Word Spotter 实现快速的上下文偏置识别，加速上下文偏置识别的同时提高了 F-score 和 WER，方法已经在 NVIDIA NeMo toolkit 中提供。