We present the corpus called HealthCall which was recorded in real-life conditions in the call center of Malakoff Humanis a health insurance company. The records include two separate audio channels, the first one for the customer and the second one for the agent. This corpus includes a transcription of the spoken conversations and was divided into three sets: training (Train), development (Dev) and test (Test) sets. Two customer relationship management tasks were assessed on the HealthCall corpus: the classification of user requests and the detection of complaints. For this purpose, we have investigated 18 feature sets: 12 linguistic and 6 audio sets. We have used BERT models for the linguistic features and Wav2Vec models for the audio features. The results show that the linguistic features give always the best results (92.7% for the Request task and 69.0% for the Complaint task) but the concatenation of acoustic and linguistic features allows a slight improvement for the Complaint task (69.4% versus 69%).

The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the Emotion Share Sub-Challenge, a regression on speech has to be made; and in the Requests Sub-Challenges, requests and complaints need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComPaRE features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectRum toolkit; in addition, wav2vec2 models are used.

The ACM Multimedia 2022 Stuttering Challenge is to determine the stuttering-related class of a speech segment. There are seven stuttering-related classes and an eighth garbage class. For this purpose, we have investigated the Wav2Vec 2.0 deep neural network to extract audio features from Transformer embeddings. Experiments were conducted on a part of the Kassel State of Fluency Corpus (KSoF). First, we introduced 21 functionals allowing to define two large composite audio feature: the first one set called W2V2 Basic audio-feature set (193,536 features) from the Base version of Transformer embeddings and the second one called W2V2 Large audio-feature set (516, 096 features) from the Large version of Transformer embeddings. Some functionals aim at estimating the spatial variability (e.g., mean, standard deviation, quartiles) and others aim at the temporal variability (e.g., linear regression slope). Then, each composite audio feature set have been splitted into a set of audio feature sets by grouping audio features by functional and by layer. Then, the most discriminant audio feature sets have been selected from these audio feature sets. Finally, two audio features sets specializing in stuttering speech have been developed and assessed: W2V2 Advanced audio-feature set (9,984 features) and W2V2 Large Advanced audio-feature set (15,360 features). Experiments have shown an improvement of 9.3% to the first one and 11.9% for the second one on the Test set compared to the official baseline of the Challenge (40.4%).