We propose a novel neural network architecture based on conformer transducer
that adds contextual information flow to the ASR systems. Our method improves
the accuracy of recognizing uncommon words while not harming the word error
rate of regular words. We explore the uncommon words accuracy improvement when
we use the new model and/or shallow fusion with context language model. We
found that combination of both provides cumulative gain in uncommon words
recognition accuracy.

我们提出了一种基于转录者的新型神经网络架构，通过增加上下文信息流来提高自动语音识别系统的准确性，特别是对于不常见的词语识别准确性的改进。我们研究了使用该新模型和 / 或与上下文语言模型浅层融合时不常见词的准确性改进，并发现两者的组合在不常见词识别准确性上具有累计增益。

自洽的上下文感知转移学习器用于语音识别

Self-consistent context aware conformer transducer for speech  recognition

Speech data from different domains has distinct acoustic and linguistic
characteristics. It is common to train a single multidomain model such as a
Conformer transducer for speech recognition on a mixture of data from all
domains. However, changing data in one domain or adding a new domain would
require the multidomain model to be retrained. To this end, we propose a
framework called modular domain adaptation (MDA) that enables a single model to
process multidomain data while keeping all parameters domain-specific, i.e.,
each parameter is only trained by data from one domain. On a streaming
Conformer transducer trained only on video caption data, experimental results
show that an MDA-based model can reach similar performance as the multidomain
model on other domains such as voice search and dictation by adding per-domain
adapters and per-domain feed-forward networks in the Conformer encoder.

本文提出了一种名为模块化域自适应的框架，通过向 Conformer 编码器中添加适合各个域的适配器和前馈网络，使单个 Conformer transducer 模型能够处理各个领域的语音数据，从而大幅提升了语音识别的性能。

基于模块化领域自适应的 Conformer 流式自动语音识别

Modular Domain Adaptation for Conformer-Based Streaming ASR

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。