When calls go wrong: how to detect problematic calls based on log-files and emotions?
When calls go wrong: how to detect problematic calls based on log-files and emotions?
复制标题
当呼叫出错时：如何根据日志文件和情绪检测有问题的呼叫？
DOI：
10.21437/interspeech.2008-76
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
J. Liscombe
中科院分区：
文献类型：
--
作者：
Ota Herm;Alexander Schmitt;J. Liscombe
传统上，在呼叫中心的交互式语音响应系统中，对问题呼叫的预测要么基于对话状态转换和识别日志数据，要么基于呼叫者的情绪。我们提出了一种综合了这两种特征集的组合模型，在人机对话中，仅在fi第一轮四轮后，问题和非问题呼叫的fi分类准确率就达到了79.22%。我们发现，使用声学特征来指示呼叫者的情绪并没有产生任何显著的fi不能提高准确率。
Traditionally, the prediction of problematic calls in Inter-active Voice Response systems in call centers has been based either on dialog state transitions and recognition log data, or on caller emotion. We present a combined model incorporating both types of feature sets that achieved 79.22% classification accuracy of problematic and non-problematic calls after only the first four turns in a human-computer dialogue. We found that using acoustic features to indicate caller emotion did not yield any significant increase of accuracy.