Addressing the scarcity of annotated data for Other-Initiated Repair (OIR), when recipients interrupt conversation progressivity to signal trouble, prompting speakers to provide repair, this work introduces OIR annotations for the NOXI corpus, achieving considerable reliability. We evaluate whether LLMs can reliably annotate OIR sequences using structured Chain-of-Thought prompting and conduct comparative analysis across two corpora: NOXI (natural dialogue) and CABB-S (Dutch, task-oriented), finding weak alignment between LLMs and human annotations, particularly in recognizing trouble-signaling. Analyzing human-LLM disagreement using the LLM-generated explanations revealed limitations: models rely on lexical patterns rather than conversational context, construct reasonable-sounding but misleading narratives, highlighting crucial limitations for both automated annotation of complex interactional phenomena.

Maintaining mutual understanding is a key component in human-human conversation to avoid conversation breakdowns, in which repair, particularly Other-Initiated Repair (OIR, when one speaker signals trouble and prompts the other to resolve), plays a vital role. However, Conversational Agents (CAs) still fail to recognize user repair initiation, leading to breakdowns or disengagement. This work proposes a multimodal model to automatically detect repair initiation in Dutch dialogues by integrating linguistic and prosodic features grounded in Conversation Analysis. The results show that prosodic cues complement linguistic features and significantly improve the results of pretrained text and audio embeddings, offering insights into how different features interact. Future directions include incorporating visual cues, exploring multilingual and cross-context corpora to assess the robustness and generalizability.

In daily conversations, people often encounter problems prompting conversational repair to enhance mutual understanding. By employing an automatic coreference solver, alongside examining repetition, we identify various linguistic features that distinguish turns when the addressee initiates repair from those when they do not. Our findings reveal distinct patterns that characterize the repair sequence and each type of repair initiation.

The CINEMO corpus of French emotional speech provides a richly annotated resource to help overcome the apparent lack of learning and testing speech material for complex, i.e. blended or mixed emotions. The protocol for its collection was dubbing selected emotional scenes from French movies. 51 speakers are contained and the total speech time amounts to 2 hours and 13 minutes and 4k speech chunks after segmentation. Extensive labelling was carried out in 16 categories for major and minor emotions and in 6 continuous dimensions. In this contribution we give insight into the corpus statistics focusing in particular on the topic of complex emotions, and provide benchmark recognition results obtained in exemplary large feature space evaluations. In the result the labelling oft he collected speech clearly demonstrates that a complex handling of emotion seems needed. Further, the automatic recognition experiments provide evidence that the automatic recognition of blended emotions appears to be feasible.