Contrastive Language–Audio Pretraining (CLAP) models learn by aligning audio and text in a shared embedding space, enabling powerful zero-shot recognition. However, their performance is highly sensitive to prompt formulation and language nuances, and they often inherit semantic ambiguities and spurious correlations from noisy pretraining data. While prior work has explored prompt engineering, adapters, and prefix tuning to address these limitations, the use of structured prior knowledge remains largely unexplored. We present iKnow-audio, a framework that integrates knowledge graphs with audio-language models to provide robust semantic grounding. iKnow-audio builds on the Audio-centric Knowledge Graph (AKG), which encodes ontological relations comprising semantic, causal, and taxonomic connections reflective of everyday sound scenes and events. By training knowlege graph embedding models on the AKG and refining CLAP predictions through this structured knowledge, iKnow-audio improves disambiguation of acoustically similar sounds and reduces reliance on prompt engineering. Comprehensive zero-shot evaluations across six benchmark datasets demonstrate consistent gains over baseline CLAP, supported by embedding-space analyses that highlight improved relational grounding. Resources are publicly available at https://github.com/michelolzam/iknow-audio

In this paper, we present the process we used in order to collect new annotations of opinions over the multimodal corpus SEMAINE composed of dyadic interactions. The dataset had already been annotated continuously in two affective dimensions related to the emotions: Valence and Arousal. We annotated the part of SEMAINE called Solid SAL composed of 79 interactions between a user and an operator playing the role of a virtual agent designed to engage a person in a sustained, emotionally colored conversation. We aligned the audio at the word level using the available high-quality manual transcriptions. The annotated dataset contains 5627 speech turns for a total of 73,944 words, corresponding to 6 hours 20 minutes of dyadic interactions. Each interaction has been labeled by three annotators at the speech turn level following a three-step process. This method allows us to obtain a precise annotation regarding the opinion of a speaker. We obtain thus a dataset dense in opinions, with more than 48% of the annotated speech turns containing at least one opinion. We then propose a new baseline for the detection of opinions in interactions improving slightly a state of the art model with RoBERTa embeddings. The obtained results on the database are promising with a F1-score at 0.72.

The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network based opinion miners. Unfortunately, gathering reliable data on which a model can be trained is notoriously difficult and existing works rely only on coarsely labeled opinions. In this work we aim at bridging the gap separating fine grained opinion models already developed for written language and coarse grained models developed for spontaneous multimodal opinion mining. We take advantage of the implicit hierarchical structure of opinions to build a joint fine and coarse grained opinion model that exploits different views of the opinion expression. The resulting model shares some properties with attention-based models and is shown to provide competitive results on a recently released multimodal fine grained annotated corpus.