Building a recognition system of speech emotion and emotional states

Xiaoyan Feng; Junzo Watada

doi:10.1109/RVSP.2013.64

Building a recognition system of speech emotion and emotional states

Xiaoyan Feng, Junzo Watada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

To make a decision in companies or public organizations, the priority ordering plays an essential. For example, their discussion is essential for stakeholder to achieve mutual consensus,. In the discussion, the difference among consensus building processes can affect the last conclusion. Therefore, it is necessary for analysis to find critical remarks reaching the consensus ("focus remark"). However, it is a basis to confirm the gfocus remark" that the consensus building process can understand exactly from the disagreement state consent and detailed exposition parties. The consensus discussion is very helpful to promote interaction by the speech. The paper addresses the design of recognition system and results are achieved by means of MFCC (Mel Frequency Campestral Coefficients) and HMM (Hidden Markov Model). Results in recognition of six emotion patterns obtained 86.8% recognition rate. According to the relation of emotional states and emotions we analyzed the support more objectively.

Original language	English
Title of host publication	Proceedings - 2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013
Publisher	IEEE Computer Society
Pages	253-258
Number of pages	6
ISBN (Print)	9781479931842
DOIs	https://doi.org/10.1109/RVSP.2013.64
Publication status	Published - 2013
Event	2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013 - Kitakyushu Duration: 2013 Dec 10 → 2013 Dec 12

Other

Other	2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013
City	Kitakyushu
Period	13/12/10 → 13/12/12

Keywords

Emotion recognition
HMM
MFCC
Praat
Speech
Support states

ASJC Scopus subject areas

Artificial Intelligence
Signal Processing

Access to Document

10.1109/RVSP.2013.64

Cite this

Feng, X & Watada, J 2013, Building a recognition system of speech emotion and emotional states. in Proceedings - 2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013., 6830024, IEEE Computer Society, pp. 253-258, 2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013, Kitakyushu, 13/12/10. https://doi.org/10.1109/RVSP.2013.64

@inproceedings{bae6749bd4f242ae99933d4a8a705661,

title = "Building a recognition system of speech emotion and emotional states",

abstract = "To make a decision in companies or public organizations, the priority ordering plays an essential. For example, their discussion is essential for stakeholder to achieve mutual consensus,. In the discussion, the difference among consensus building processes can affect the last conclusion. Therefore, it is necessary for analysis to find critical remarks reaching the consensus ({"}focus remark{"}). However, it is a basis to confirm the gfocus remark{"} that the consensus building process can understand exactly from the disagreement state consent and detailed exposition parties. The consensus discussion is very helpful to promote interaction by the speech. The paper addresses the design of recognition system and results are achieved by means of MFCC (Mel Frequency Campestral Coefficients) and HMM (Hidden Markov Model). Results in recognition of six emotion patterns obtained 86.8% recognition rate. According to the relation of emotional states and emotions we analyzed the support more objectively.",

keywords = "Emotion recognition, HMM, MFCC, Praat, Speech, Support states",

author = "Xiaoyan Feng and Junzo Watada",

year = "2013",

doi = "10.1109/RVSP.2013.64",

language = "English",

isbn = "9781479931842",

pages = "253--258",

booktitle = "Proceedings - 2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013",

publisher = "IEEE Computer Society",

note = "2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013 ; Conference date: 10-12-2013 Through 12-12-2013",

}

TY - GEN

T1 - Building a recognition system of speech emotion and emotional states

AU - Feng, Xiaoyan

AU - Watada, Junzo

PY - 2013

Y1 - 2013

N2 - To make a decision in companies or public organizations, the priority ordering plays an essential. For example, their discussion is essential for stakeholder to achieve mutual consensus,. In the discussion, the difference among consensus building processes can affect the last conclusion. Therefore, it is necessary for analysis to find critical remarks reaching the consensus ("focus remark"). However, it is a basis to confirm the gfocus remark" that the consensus building process can understand exactly from the disagreement state consent and detailed exposition parties. The consensus discussion is very helpful to promote interaction by the speech. The paper addresses the design of recognition system and results are achieved by means of MFCC (Mel Frequency Campestral Coefficients) and HMM (Hidden Markov Model). Results in recognition of six emotion patterns obtained 86.8% recognition rate. According to the relation of emotional states and emotions we analyzed the support more objectively.

AB - To make a decision in companies or public organizations, the priority ordering plays an essential. For example, their discussion is essential for stakeholder to achieve mutual consensus,. In the discussion, the difference among consensus building processes can affect the last conclusion. Therefore, it is necessary for analysis to find critical remarks reaching the consensus ("focus remark"). However, it is a basis to confirm the gfocus remark" that the consensus building process can understand exactly from the disagreement state consent and detailed exposition parties. The consensus discussion is very helpful to promote interaction by the speech. The paper addresses the design of recognition system and results are achieved by means of MFCC (Mel Frequency Campestral Coefficients) and HMM (Hidden Markov Model). Results in recognition of six emotion patterns obtained 86.8% recognition rate. According to the relation of emotional states and emotions we analyzed the support more objectively.

KW - Emotion recognition

KW - HMM

KW - MFCC

KW - Praat

KW - Speech

KW - Support states

UR - http://www.scopus.com/inward/record.url?scp=84903630909&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903630909&partnerID=8YFLogxK

U2 - 10.1109/RVSP.2013.64

DO - 10.1109/RVSP.2013.64

M3 - Conference contribution

AN - SCOPUS:84903630909

SN - 9781479931842

SP - 253

EP - 258

BT - Proceedings - 2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013

PB - IEEE Computer Society

T2 - 2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013

Y2 - 10 December 2013 through 12 December 2013

ER -

Building a recognition system of speech emotion and emotional states

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this