Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models
Computation and Language
2020-03-06 v2 Machine Learning
Audio and Speech Processing
Abstract
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. Our approach improves the-state-of-the-art accuracy on IEMOCAP from 66.6% to 71.7%, and achieves an accuracy of 70.10% on SWBD-sentiment with more than 49,500 utterances.
Cite
@article{arxiv.1911.09762,
title = {Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models},
author = {Zhiyun Lu and Liangliang Cao and Yu Zhang and Chung-Cheng Chiu and James Fan},
journal= {arXiv preprint arXiv:1911.09762},
year = {2020}
}