  Traditional topic identification solutions from audio rely on an automatic
speech recognition system (ASR) to produce transcripts used as input to a
text-based model. These approaches work well in high-resource scenarios, where
there are sufficient data to train both components of the pipeline. However, in
low-resource situations, the ASR system, even if available, produces
low-quality transcripts, leading to a bad text-based classifier. Moreover,
spontaneous speech containing hesitations can further degrade the performance
of the ASR model. In this paper, we investigate alternatives to the standard
text-only solutions by comparing audio-only and hybrid techniques of jointly
utilising text and audio features. The models evaluated on spontaneous Finnish
speech demonstrate that purely audio-based solutions are a viable option when
ASR components are not available, while the hybrid multi-modal solutions
achieve the best results.
