Attention-based contextual biasing approaches have shown significant
improvements in the recognition of generic and/or personal rare-words in
End-to-End Automatic Speech Recognition (E2E ASR) systems like neural
transducers. These approaches employ cross-attention to bias the model towards
specific contextual entities injected as bias-phrases to the model. Prior
approaches typically relied on subword encoders for encoding the bias phrases.
However, subword tokenizations are coarse and fail to capture granular
pronunciation information which is crucial for biasing based on acoustic
similarity. In this work, we propose to use lightweight character
representations to encode fine-grained pronunciation features to improve
contextual biasing guided by acoustic similarity between the audio and the
contextual entities (termed acoustic biasing). We further integrate pretrained
neural language model (NLM) based encoders to encode the utterance's semantic
context along with contextual entities to perform biasing informed by the
utterance's semantic context (termed semantic biasing). Experiments using a
Conformer Transducer model on the Librispeech dataset show a 4.62% - 9.26%
relative WER improvement on different biasing list sizes over the baseline
contextual model when incorporating our proposed acoustic and semantic biasing
approach. On a large-scale in-house dataset, we observe 7.91% relative WER
improvement compared to our baseline model. On tail utterances, the
improvements are even more pronounced with 36.80% and 23.40% relative WER
improvements on Librispeech rare words and an in-house testset respectively.

研究提出一种轻量级字符表示的方法来编码精细的发音特征，以提高基于声学相似性的情境偏倚，在与音频和情境实体相关的语义上执行情境偏倚，并集成预训练的神经语言模型（NLM）。在 Librispeech 数据集上进行的实验表明，在不同的情境偏见列表大小上，采用提出的声学偏倚和语义偏倚方法，相对于基线情境模型，Conformer Transducer 模型相对 WER 提高了 4.62％-9.26％。在大规模的内部数据集上，相对于基准模型，相对 WER 的提高为 7.91％。在 Librispeech 稀有单词和内部测试集上，尾部话语的表现甚至更加显著，分别实现了 36.80％和 23.40％的相对 WER 改善。