基于混合指针网络语言模型的ASR格重核
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model
摘要：
在社交媒体上上传的视频通常附有文字描述。在为视频构建自动语音识别（ASR）系统时，我们可以利用由这样的视频元数据提供的上下文信息。在本文中，我们探索ASR格rescoring有选择地参加视频描述。我们首先使用基于注意力的方法来提取视频元数据的上下文矢量表示，并使用这些表示作为输入的一部分，以神经语言模型在格重新评分。其次，我们提出了一种混合指针网络的方法来显式地插入元数据中的词出现的词概率。我们对语言建模和ASR任务进行了实验评估，并证明了这两种方法通过选择性地利用视频元数据来提高性能。
英文摘要：
Videos uploaded on social media are often accompanied with textual descriptions. In building automatic speech recognition (ASR) systems for videos, we can exploit the contextual information provided by such video metadata. In this paper, we explore ASR lattice rescoring by selectively attending to the video descriptions. We first use an attention based method to extract contextual vector representations of video metadata, and use these representations as part of the inputs to a neural language model during lattice rescoring. Secondly, we propose a hybrid pointer network approach to explicitly interpolate the word probabilities of the word occurrences in metadata. We perform experimental evaluations on both language modeling and ASR tasks, and demonstrate that both proposed methods provide performance improvements by selectively leveraging the video metadata.
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。
