Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models

Understand

In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task.

• We show that end-to-end ASR features, which integrate both acoustic and text information from speech, achieve promising results.
• We use RNN with self-attention as the sentiment classifier, which also provides an easy visualization through attention weights to help interpret model predictions.
• We use well benchmarked IEMOCAP dataset and a new large-scale speech sentiment dataset SWBD-sentiment for evaluation.