Integrating recognition confidence scoring with language understanding and dialogue modeling
Integrating recognition confidence scoring with language understanding and dialogue modeling
复制标题
将识别置信度评分与语言理解和对话建模相结合
DOI：
10.21437/icslp.2000-451
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
S. Seneff
中科院分区：
文献类型：
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作者：
Timothy J. Hazen;Theresa Burianek;J. Polifroni;S. Seneff
在本文中，我们提出了一种方法，将置信度分数的理解和对话组件的语音理解系统。我们的系统的理解组件接收一个n-最好的识别假设的列表，增强字级的置信度分数。当识别器的n-最佳列表中的单词被错误识别时，理解组件使用置信度分数来假设。理解组件具有基于周围上下文预测误识别的单词的语义类别的能力，并且还建议用户何时应该重新确认可能被误解的关键词。理解组件的输出被传递到对话控制组件上，该对话控制组件可以对理解组件做出的各种建议采取行动。为了评估该系统，使用JUPITER气象信息系统进行了实验。在理解水平上使用键值对概念错误率作为评价指标进行评价。当单词置信度分数被整合到理解组件中时，概念错误率降低了35%。
In this paper we present a method for integrating confidence scores into the understanding and dialogue components of a speech understanding system. The understanding component of our system receives an n-best list of recognition hypotheses augmented with word-level confidence scores. The confidence scores are used by the understanding component to hypothesize when words in a recognizer’s n-best list have been misrecognized. The understanding component has the ability to predict the semantic class of misrecognized words based on the surrounding context and also to suggest when key words which may have been misunderstood should be re-confirmed by the user. The output of the understanding component is passed onto a dialogue control component which can act on various suggestions made by the understanding component. To evaluate the system, experiments were conducted using the JUPITER weather information system. Evaluation was performed at the understanding level using key-value pair concept error rate as the evaluation metric. When word confidence scores were integrated into the understanding component, the concept error rate was reduced by 35%.