Year: 2024
Type: conference-paper
Abstract: Contrastive language-audio pretraining (CLAP) has recently emerged as a method for making audio analysis more generalisable.Specifically, CLAP-style models are able to 'answer' a diverse set of language queries, extending the capabilities of audio models beyond a closed set of labels.However, CLAP relies on a large set of (audio, query) pairs for pretraining.While such sets are available for general audio tasks, like captioning or sound event detection, there are no datasets with matched audio and text queries for computational paralinguistic (CP) tasks.As a result, the community relies on generic CLAP models trained for general audio with limited success.In the present study, we explore training considerations for ParaCLAP, a CLAP-style model suited to CP, including a novel process for creating audio-language queries.We demonstrate its effectiveness on a set of computational paralinguistic tasks, where it is shown to surpass the performance of open-source state-of-theart models.Our code and resources are publicly available at: https://github.com/KeiKinn/ParaCLAP
Source: Interspeech (USB)
Institutions: University of Augsburg, Technical University of Munich
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4402111518