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Human-Machine Corpus Analysis for Generation and Interaction with Spoken Dialog Systems

机译：与口头对话系统产生和互动的人机语料库分析

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This paper describes a new approach to language generation for simulated users based on the construction of flexible templates extracted from a corpus. In our opinion a realistic user simulation on the speech level is based on two parts: user behavior and language generation. In this work we mainly concentrate on the language generation for simulated user interaction with spoken dialog systems (SDS). The presented approach could be used as part of a user simulation for intensive end-to-end system tests and evaluations and for testing purposes of the speech recognition and natural language understanding modules of an SDS. We present our semi-automatic analysis of a human-machine corpus, the corpus-based language generation process, which generates realistic user replies on the basis of their usage frequency and verbosity, and a speech enrichment approach to increase the variability of the output. We demonstrate in user simulation experiments realized with synthesized speech, that the generated output is comparable in its variability to the utterances of human testers.
机译：本文介绍了基于从语料库中提取的柔性模板构造的模拟用户的语言生成的新方法。在我们看来，语音级别的现实用户仿真基于两部分：用户行为和语言生成。在这项工作中，我们主要专注于模拟用户交互与口头对话系统（SDS）的语言生成。所提出的方法可以用作用于密集的端到端系统测试和评估的用户仿真的一部分，以及用于测试SDS的语音识别和自然语言模块的测试目的。我们介绍了对人机语料库的半自动分析，基于语料库的语言生成过程，它基于其使用频率和冗长来生成现实用户回复，以及提高输出的可变性的语音富集方法。我们在使用合成语音实现的用户仿真实验中证明，所产生的输出在其对人体测试仪的话语中的可变性中可比。

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