  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.
