logo

For the Inventor, By the Inventor

The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 12100418 B1

PDFFull Text
Date of Patent:
Sep. 24, 2024

Filed:

Sep. 10, 2021

Dialogue emotion correction method based on graph neural network

Applicant:

Institute of Automation, Chinese Academy of Sciences, Beijing, CN;

Inventors:

Jianhua Tao, Beijing, CN;

Zheng Lian, Beijing, CN;

Bin Liu, Beijing, CN;

Xuefei Liu, Beijing, CN;

Assignee:

INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES, Beijing, CN;

Attorney:

Oliff PLC

Primary Examiner:

Hai Phan

Assistant Examiner:

Alvin Iskender

Int. Cl.
CPC ...
G10L 25/63 (2013.01); G06F 18/25 (2023.01); G06F 40/166 (2020.01); G06F 40/211 (2020.01); G06F 40/216 (2020.01); G06F 40/284 (2020.01); G06F 40/289 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06V 20/40 (2022.01); G06V 40/16 (2022.01); G10L 15/02 (2006.01); G10L 15/26 (2006.01); G10L 25/30 (2013.01);
U.S. Cl.
CPC ...
G10L 25/63 (2013.01); G06F 18/253 (2023.01); G06F 40/166 (2020.01); G06F 40/211 (2020.01); G06F 40/216 (2020.01); G06F 40/284 (2020.01); G06F 40/289 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06V 20/41 (2022.01); G06V 40/166 (2022.01); G06V 40/168 (2022.01); G10L 15/02 (2013.01); G10L 15/26 (2013.01); G10L 25/30 (2013.01);
Abstract

Disclosed is a dialogue emotion correction method based on a graph neural network, including: extracting acoustic features, text features, and image features from a video file to fuse them into multi-modal features; obtaining an emotion prediction result of each sentence of a dialogue in the video file by using the multi-modal features; fusing the emotion prediction result of each sentence with interaction information between talkers in the video file to obtain interaction information fused emotion features; combining, on the basis of the interaction information fused emotion features, with context-dependence relationship in the dialogue to obtain time-series information fused emotion features; correcting, by using the time-series information fused emotion features, the emotion prediction result of each sentence that is obtained previously as to obtain a more accurate emotion recognition result.

Find Patent Forward Citations

Loading…