NDLI: Late fusion of individual engines for improved recognition of negative emotion in speech

Content Provider	IEEE Xplore Digital Library
Author	Schuller, B. Metze, F. Steidl, S. Batliner, A. Eyben, F. Polzehl, T.
Copyright Year	2010
Description	Author affiliation: Language Technologies Institute, Carnegie Mellon University (CMU), Pittsburgh, PA, USA (Metze, F.) \|\| Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany (Steidl, S.; Batliner, A.) \|\| Usability and Quality Lab, Technische Universität Berlin (T-Labs), Germany (Polzehl, T.) \|\| Institute for Human-Machine Communication, Technische Universität München (TUM), Germany (Schuller, B.; Eyben, F.)
Abstract	The fusion of multiple recognition engines is known to be able to outperform individual ones, given sufficient independence of methods, models, and knowledge sources. We therefore investigate late fusion of different speech-based recognizers of emotion. Two generally different streams of information are considered: acoustics and linguistics fed by state-of-the-art automatic speech recognition. A total of five emotion recognition engines from different sites that provide heterogeneous output information are integrated by either simple democratic vote or learning ‘which predictor to trust when’. We are able to significantly outperform the best individual engine by fusion, and the so far best reported result on the recently introduced Emotion Challenge task.
Starting Page	5230
Ending Page	5233
File Size	77003
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5494986
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Engines Emotion recognition Speech recognition Voting Automatic speech recognition Spatial databases Robots Man machine systems Natural languages Usability Speech Analysis Emotion Recognition Late Fusion
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Late fusion of individual engines for improved recognition of negative emotion in speech – learning vs. Democratic vote.

LATE FUSION OF INDIVIDUAL ENGINES FOR IMPROVED RECOGNITION OF NEGATIVE EMOTION IN SPEECH – LEARNING VS. DEMOCRATIC VOTE

Late fusion of individual engines for imrpoved recognition of negative emotion in speech – learning vs. democratic vote

Speech Emotion Recognition Using MFCCs Extracted from a Mobile Terminal based on ETSI Front End

Learning with synthesized speech for automatic emotion recognition

Emotion pitch variation analysis in Malay and English voice samples

Emotion Recognition and Conversion for Mandarin Speech

Using human-machine dialog to query shortcut on the map

Application of dimensional emotion model in automatic emotional speech recognition

Late fusion of individual engines for improved recognition of negative emotion in speech - learning vs. democratic vote

Similar Documents

Late fusion of individual engines for improved recognition of negative emotion in speech – learning vs. Democratic vote.

LATE FUSION OF INDIVIDUAL ENGINES FOR IMPROVED RECOGNITION OF NEGATIVE EMOTION IN SPEECH – LEARNING VS. DEMOCRATIC VOTE

Late fusion of individual engines for imrpoved recognition of negative emotion in speech – learning vs. democratic vote

Speech Emotion Recognition Using MFCCs Extracted from a Mobile Terminal based on ETSI Front End

Learning with synthesized speech for automatic emotion recognition

Emotion pitch variation analysis in Malay and English voice samples

Emotion Recognition and Conversion for Mandarin Speech

Using human-machine dialog to query shortcut on the map

Application of dimensional emotion model in automatic emotional speech recognition

Late fusion of individual engines for improved recognition of negative emotion in speech - learning vs. democratic vote