A Dialogical Emotion Decoder for Speech Motion Recognition in Spoken Dialog
Sung-Lin Yeh, Yun-Shao Lin, Chi-Chun Lee
Abstract
Developing a robust emotion speech recognition (SER) system for human dialog is important in advancing conversational agent design. In this paper, we proposed a novel inference algorithm, a dialogical emotion decoding (DED) algorithm, that treats a dialog as a sequence and consecutively decode the emotion states of each utterance over time with a given recognition engine. This decoder is trained by incorporating intra- and inter-speakers emotion influences within a conversation. Our approach achieves a 70.1% in four class emotion on the IEMOCAP database, which is 3% over the state-of-art model. The evaluation is further conducted on a multi-party interaction database, the MELD, which shows a similar effect. Our proposed DED is in essence a conversational emotion rescoring decoder that can also be flexibly combined with different SER engines.
BibTeX
@inproceedings{icassp2020_adialogicalemoti,
title = {A Dialogical Emotion Decoder for Speech Motion Recognition in Spoken Dialog},
author = {Sung-Lin Yeh and Yun-Shao Lin and Chi-Chun Lee},
booktitle = {ICASSP 2020},
year = {2020}
}