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Speech emotion recognition (SER) models typically rely on costlyhuman-labeled data for training, making scaling methods to large speechdatasets and nuanced emotion taxonomies difficult. We present LanSER, a methodthat enables the use of unlabeled data by inferring weak emotion labels viapre-trained large language models through weakly-supervised learning. Forinferring weak labels constrained to a taxonomy, we use a textual entailmentapproach that selects an emotion label with the highest entailment score for aspeech transcript extracted via automatic speech recognition. Our experimentalresults show that models pre-trained on large datasets with this weaksupervision outperform other baseline models on standard SER datasets whenfine-tuned, and show improved label efficiency. Despite being pre-trained onlabels derived only from text, we show that the resulting representationsappear to model the prosodic content of speech.

LanSER: Language-Model Supported Speech Emotion Recognition