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- W4308245298 abstract "Embedding paralinguistic properties is a challenging task as there are only a few hours of training data available for domains such as emotional speech. One solution to this problem is to pretrain a general self-supervised speech representation model on large amounts of unlabeled speech. This pretrained model is then finetuned to a specific task. Paralinguistic properties however have notoriously high class variance, making the finetuning ineffective. In this work, we propose a two step approach to this. First we improve the embedding space, then we train an adapter to bridge the gap from the embedding space to a classification task. In order to improve the class invariance we use a combination of contrastive and non-contrastive losses to explicitly optimize for class invariant, yet discriminative features. Our approach consistently outperforms baselines that are finetuned end-to-end on multiple tasks and surpasses a benchmark on state-of-the-art emotion classification." @default.
- W4308245298 created "2022-11-09" @default.
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- W4308245298 date "2022-10-21" @default.
- W4308245298 modified "2023-09-27" @default.
- W4308245298 title "Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis" @default.
- W4308245298 doi "https://doi.org/10.48550/arxiv.2211.01964" @default.
- W4308245298 hasPublicationYear "2022" @default.
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