Robust dialogue-state dependent language modeling using leaving-one-out
Robust dialogue-state dependent language modeling using leaving-one-out
复制标题
使用留一法的鲁棒对话状态相关语言建模
DOI：
10.1109/icassp.1999.759773
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
A. Baader
中科院分区：
文献类型：
--
作者：
F. Wessel;A. Baader
在自动查询系统中使用依赖于对话状态的语言模型，如果合理地预测对话状态是可行的，则可以提高语音识别和理解。在本文中，对话状态被定义为包含在系统提示中的参数集合。对于每个对话状态，构建单独的语言模型。为了获得强大的语言模型，尽管少量的训练数据，我们建议插入所有的对话状态依赖的语言模型线性每个对话状态，并训练大量的EM算法与留一法相结合的插值权重。我们提出了一个小的荷兰语语料库，已记录在荷兰的火车时刻表信息系统的实验结果，并显示的困惑和单词错误率可以显着降低。
The use of dialogue-state dependent language models in automatic inquiry systems can improve speech recognition and understanding if a reasonable prediction of the dialogue state is feasible. In this paper, the dialogue state is defined as the set of parameters which are contained in the system prompt. For each dialogue state a separate language model is constructed. In order to obtain robust language models despite the small amount of training data we propose to interpolate all of the dialogue-state dependent language models linearly for each dialogue state and to train the large number of resulting interpolation weights with the EM-algorithm in combination with leaving-one-out. We present experimental results on a small Dutch corpus which has been recorded in the Netherlands with a train timetable information system and show that the perplexity and the word error rate can be reduced significantly.