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.

利用先前训练的大型语言模型，LanSER 通过弱监督学习方法通过推断弱情绪标签实现对未标记数据的利用，并在限制到特定分类方法的情况下使用文本蕴涵方法从自动语音识别中提取的语音转录本选择具有最高蕴涵分数的情绪标签，实验证明，使用这种弱监督方法的大型数据集的预训练模型在标准语音情感识别数据集上的调整后表现优于其他基线模型，并显示了改进的标签效率，尽管这些模型仅以文本为基础进行了预训练，但我们证明了得到的表示形式似乎对语音的韵律内容进行了建模。