This paper presents an extension to train end-to-end Context-Aware Transformer Transducer ( CATT ) models by using a simple, yet efficient method of mining hard negative phrases from the latent space of the context encoder. During training, given a reference query, we mine a number of similar phrases using approximate nearest neighbour search. These sampled phrases are then used as negative examples in the context list alongside random and ground truth contextual information. By including approximate nearest neighbour phrases (ANN-P) in the context list, we encourage the learned representation to disambiguate between similar, but not identical, biasing phrases. This improves biasing accuracy when there are several similar phrases in the biasing inventory. We carry out experiments in a large-scale data regime obtaining up to 7% relative word error rate reductions for the contextual portion of test data. We also extend and evaluate CATT approach in streaming applications.

翻译：本文提出了一种扩展方法，通过从上下文编码器的潜在空间中挖掘困难负例短语，以训练端到端上下文感知Transformer换能器（CATT）模型。该方法在训练时对参考查询进行近似近邻搜索，挖掘若干相似短语，并将这些采样短语与随机及真实上下文信息一同作为上下文列表中的负例。通过在上下文列表中纳入近似近邻短语（ANN-P），我们促使学习到的表征能够区分相似但不相同的偏置短语。当偏置库中存在多个相似短语时，这能提升偏置准确性。我们在大规模数据场景下开展实验，测试数据中上下文部分获得了最高7%的相对词错误率降低。同时，我们还将CATT方法扩展并评估其在流式应用中的表现。