TY  - CONF
Y1  - 2012///
SN  - 9781467319386
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867918939&doi=10.1109%2fICCISci.2012.6297301&partnerID=40&md5=5d16ad70ef5e2602b8509e95ea8abde1
A1  - Mohan Kudiri, K.
A1  - Md Said, A.
A1  - Nayan, M.Y.
VL  - 1
EP  - 525
CY  - Kuala Lumpur
AV  - none
N2  - Automatic speech recognition analysis has been an active part in computer science for more than two decades. In general, to detect an emotion, long continuous signal is needed. Relative amplitude reduces bias of glottal mutation of speech wave amplitude and obtains a normalized measure without concern of information from being distinct in feature. Nonverbal communication plays crucial role in human-human or human-machine interpersonal relationships. In this paper, we propose the use of relative bin frequency coefficients for speech signal segmentation. Here, the support vector machine classifier is used to implement automatic emotion detection system. © 2012 IEEE.
N1  - cited By 6; Conference of 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93334
KW  - Active parts; Automatic speech recognition; Emotion detection; Frequency coefficient; Frequency features; Human-machine; Interpersonal relationship; Non-verbal communications; Relative amplitude; relative sub-image based features; Speech signals; Wave amplitudes
KW  -  Communication; Information science; Support vector machines; Technology
KW  -  Bins
ID  - scholars2782
TI  - Emotion detection using relative amplitude-based features through speech
SP  - 522
ER  -