Speaker-Characterized Emotion Recognition using Online and Iterative Speaker Adaptation

Title
Speaker-Characterized Emotion Recognition using Online and Iterative Speaker Adaptation
Author(s)
박정식Kim, Jae-Bok[Kim, Jae-Bok]Oh, Yung-Hwan[Oh, Yung-Hwan]
Keywords
HIDDEN MARKOV-MODELS; SPEECH RECOGNITION
Issue Date
201212
Publisher
SPRINGER
Citation
COGNITIVE COMPUTATION, v.4, no.4, pp.398 - 408
Abstract
This paper proposes a novel speech emotion recognition (SER) framework for affective interaction between human and personal devices. Most of the conventional SER techniques adopt a speaker-independent model framework because of the sparseness of individual speech data. However, a large amount of individual data can be accumulated on a personal device, making it possible to construct speaker-characterized emotion models in accordance with a speaker adaptation procedure. In this study, to address problems associated with conventional adaptation approaches in SER tasks, we modified a representative adaptation technique, maximum likelihood linear regression (MLLR), on the basis of selective label refinement. We subsequently carried out the modified MLLR procedure in an online and iterative manner, using accumulated individual data, to further enhance the speaker-characterized emotion models. In the SER experiments based on an emotional corpus, our approach exhibited performance superior to that of conventional adaptation techniques as well as the speaker-independent model framework.
URI
http://hdl.handle.net/YU.REPOSITORY/26721http://dx.doi.org/10.1007/s12559-012-9132-9
ISSN
1866-9956
Appears in Collections:
공과대학 > 모바일정보통신공학과 > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE