Specialized language models using dialogue predictions
Specialized language models using dialogue predictions
复制标题
使用对话预测的专业语言模型
DOI：
10.1109/icassp.1997.596055
复制
发表时间：
1996
期刊：
影响因子：
--
通讯作者：
P. Baggia
中科院分区：
文献类型：
--
作者：
C. Popovici;P. Baggia
分析口语对话系统中访问数据库的语言建模。通过利用对话预测获得的几个语言模型的使用给出了更好的结果比使用单个模型的整个对话交互。为此，已经创建了几个模型，每个模型针对特定的系统问题，例如请求或确认参数。依赖于对话的语言模型的使用提高了识别水平和理解水平的性能，特别是对系统请求的回答。此外，使用其他方法来提高性能，如词汇的自动聚类或在识别过程中使用更好的声学模型，不会影响依赖于对话的语言模型所带来的改进。在我们的实验中使用的系统是Dialogos，用于通过电话访问铁路时刻表信息的意大利口语对话系统。实验是在使用Dialogos收集的大量对话语料库上进行的。
Analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason, several models have been created, each one for a specific system question, such as the request for or the confirmation of a parameter. The use of dialogue-dependent language models increases the performance both at the recognition level and at the understanding level, especially on answers to system requests. Moreover, using other methods to increase the performance, like the automatic clustering of vocabulary words or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models. The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues collected using Dialogos.