使用局部和全局对数线性组合的早期LM集成
Early Stage LM Integration Using Local and Global Log-Linear Combination
摘要：
在自动语音识别任务中，具有隐式对齐机制（例如注意力）的序列到序列模型正在缩小与传统混合隐马尔可夫模型（HMM）的性能差距。在这两种情况下，提高单词错误率的一个重要因素是使用在大型纯文本语料库上训练的外部语言模型（LM）。语言模型集成是直接的，在经典的基于HMM的建模中，声学模型和语言模型的明确分离。与此相反，多个集成方案已被提出的注意力模型。在这项工作中，我们提出了一种新的方法，语言模型集成到隐式对齐的序列到序列模型。声学和语言模型的对数线性模型组合与每个令牌重正化。这使我们能够在训练和测试中有效地计算完整的归一化项。这是一个全球重整化方案，相当于在训练中应用浅融合相比。所提出的方法显示出良好的改进，在我们的国家的最先进的Librispeech系统的标准模型组合（浅融合）。此外，即使在训练后将LM换成更强大的LM，改进也是持久的。
英文摘要：
Sequence-to-sequence models with an implicit alignment mechanism (e.g. attention) are closing the performance gap towards traditional hybrid hidden Markov models (HMM) for the task of automatic speech recognition. One important factor to improve word error rate in both cases is the use of an external language model (LM) trained on large text-only corpora. Language model integration is straightforward with the clear separation of acoustic model and language model in classical HMM-based modeling. In contrast, multiple integration schemes have been proposed for attention models. In this work, we present a novel method for language model integration into implicit-alignment based sequence-to-sequence models. Log-linear model combination of acoustic and language model is performed with a per-token renormalization. This allows us to compute the full normalization term efficiently both in training and in testing. This is compared to a global renormalization scheme which is equivalent to applying shallow fusion in training. The proposed methods show good improvements over standard model combination (shallow fusion) on our state-of-the-art Librispeech system. Furthermore, the improvements are persistent even if the LM is exchanged for a more powerful one after training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
