论文标题
用户请求的预测和投诉在口语客户代理对话中
Prediction of User Request and Complaint in Spoken Customer-Agent Conversations
论文作者
论文摘要
我们介绍称为Healthcall的语料库。这是在Malakoff Humanis呼叫中心的现实状况中记录的。它包括两个独立的音频渠道，第一个频道和客户的第二个音频渠道。每个对话都是匿名的，尊重一般数据保护法规。该语料库包括对话对话的转录，并分为两组：火车和开发设备。在Healthcall语料库中评估了两项重要的客户关系管理任务：用户请求类型和投诉检测的类型自动预测。为此，我们研究了14个功能集：6个语言功能集，6个音频功能集和2个声乐互动功能集。我们已经为音频特征使用了来自Transformers模型的双向编码器表示，opensmile和wav2Vec 2.0。人声相互作用特征集是从转弯捕获中设计和开发的。结果表明，语言特征始终给出最佳结果（请求任务91.2％，投诉任务为70.3％）。 WAV2VEC 2.0功能似乎比Compare16功能更适合这两个任务。人声互动的特征优于投诉任务上的比较16功能，而只有六个功能达到了57％的速率。
We present the corpus called HealthCall. This was recorded in real-life conditions in the call center of Malakoff Humanis. It includes two separate audio channels, the first one for the customer and the second one for the agent. Each conversation was anonymized respecting the General Data Protection Regulation. This corpus includes a transcription of the spoken conversations and was divided into two sets: Train and Devel sets. Two important customer relationship management tasks were assessed on the HealthCall corpus: Automatic prediction of type of user requests and complaints detection. For this purpose, we have investigated 14 feature sets: 6 linguistic feature sets, 6 audio feature sets and 2 vocal interaction feature sets. We have used Bidirectional Encoder Representation from Transformers models for the linguistic features, openSMILE and Wav2Vec 2.0 for the audio features. The vocal interaction feature sets were designed and developed from Turn Takings. The results show that the linguistic features always give the best results (91.2% for the Request task and 70.3% for the Complaint task). The Wav2Vec 2.0 features seem more suitable for these two tasks than the ComPaRe16 features. Vocal interaction features outperformed ComPaRe16 features on Complaint task with a 57% rate achieved with only six features.
