Pause-Encoded Language Models for Recognition of Alzheimer's Disease and Emotion

Abstract

We propose enhancing Transformer language models (BERT, RoBERTa) to take advantage of pauses. Pauses play an important role in speech. In previous work we developed a method to encode pauses in transcripts for recognition of Alzheimer's disease. In this study, we extend this idea to language models. We re-train BERT and RoBERTa using a large collection of pause-encoded transcripts, and conduct fine- tuning for two downstream tasks, recognition of Alzheimer's disease and emotion. Pause-encoded language models outperform text-only language models on these tasks. Pause augmentation by duration perturbation for training is shown to improve pause-encoded language models.