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arXiv · 2307.11450

Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information

Dejan Porjazovski·Tamás Grósz·Mikko Kurimo
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Abstract

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.

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Dejan Porjazovski, Tamás Grósz, Mikko Kurimo. 2023-07-21. Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information. https://arxiv.org/abs/2307.11450

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