Neural contextual biasing effectively improves automatic speech recognition
(ASR) for crucial phrases within a speaker's context, particularly those that
are infrequent in the training data. This work proposes contextual text
injection (CTI) to enhance contextual ASR. CTI leverages not only the paired
speech-text data, but also a much larger corpus of unpaired text to optimize
the ASR model and its biasing component. Unpaired text is converted into
speech-like representations and used to guide the model's attention towards
relevant bias phrases. Moreover, we introduce a contextual text-injected (CTI)
minimum word error rate (MWER) training, which minimizes the expected WER
caused by contextual biasing when unpaired text is injected into the model.
Experiments show that CTI with 100 billion text sentences can achieve up to
43.3% relative WER reduction from a strong neural biasing model. CTI-MWER
provides a further relative improvement of 23.5%.

通过上下文中注入文本的方式，使用大规模未配对的文本来优化自动语音识别（ASR）模型及其偏置成分，提高重要短语的识别准确率。实验证明，使用 1000 亿个文本句子进行 CTI，相对于强大的神经偏置模型，可以实现高达 43.3% 的相对词错误率的降低。CTI-MWER 进一步提供了 23.5% 的相对改进。

神经背景性偏倚的文本插入

Text Injection for Neural Contextual Biasing

We propose a streaming non-autoregressive (non-AR) decoding algorithm to
deliberate the hypothesis alignment of a streaming RNN-T model. Our algorithm
facilitates a simple greedy decoding procedure, and at the same time is capable
of producing the decoding result at each frame with limited right context, thus
enjoying both high efficiency and low latency. These advantages are achieved by
converting the offline Align-Refine algorithm to be streaming-compatible, with
a novel transformer decoder architecture that performs local self-attentions
for both text and audio, and a time-aligned cross-attention at each layer.
Furthermore, we perform discriminative training of our model with the minimum
word error rate (MWER) criterion, which has not been done in the non-AR
decoding literature. Experiments on voice search datasets and Librispeech show
that with reasonable right context, our streaming model performs as well as the
offline counterpart, and discriminative training leads to further WER gain when
the first-pass model has small capacity.

提出了一种流式非自回归（non-AR）解码算法，其利用新型变换器解码器架构在每个帧上执行文本和音频的本地自我关注，并在每个层上进行时间对齐的交叉关注，实现了高效率和低延迟，在声音搜索数据集和 Librispeech 上的实验显示，我们的流式模型表现良好，并且在第一遍模型容量较小的情况下进行判别式训练会进一步提高词误率。