تقرير
LanSER: Language-Model Supported Speech Emotion Recognition
| العنوان: | LanSER: Language-Model Supported Speech Emotion Recognition |
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| المؤلفون: | Gong, Taesik, Belanich, Josh, Somandepalli, Krishna, Nagrani, Arsha, Eoff, Brian, Jou, Brendan |
| المصدر: | INTERSPEECH (2023) 2408-2412 |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | Speech emotion recognition (SER) models typically rely on costly human-labeled data for training, making scaling methods to large speech datasets and nuanced emotion taxonomies difficult. We present LanSER, a method that enables the use of unlabeled data by inferring weak emotion labels via pre-trained large language models through weakly-supervised learning. For inferring weak labels constrained to a taxonomy, we use a textual entailment approach that selects an emotion label with the highest entailment score for a speech transcript extracted via automatic speech recognition. Our experimental results show that models pre-trained on large datasets with this weak supervision outperform other baseline models on standard SER datasets when fine-tuned, and show improved label efficiency. Despite being pre-trained on labels derived only from text, we show that the resulting representations appear to model the prosodic content of speech. Comment: Presented at INTERSPEECH 2023 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.21437/Interspeech.2023-1832 |
| URL الوصول: | http://arxiv.org/abs/2309.03978 |
| رقم الأكسشن: | edsarx.2309.03978 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.21437/Interspeech.2023-1832 |
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