Abstract
Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.
| Original language | English |
|---|---|
| Pages (from-to) | 91-96 |
| Number of pages | 6 |
| Journal | Unknown Journal |
| Volume | 58 |
| DOIs | |
| Publication status | Published - 2015 |
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Keywords
- Emotion Analysis
- Emotion Classification
- Mel-Frequency Cepstral Coefficients
- Speech Processing
ASJC Scopus subject areas
- Computer Science(all)
Cite this
Detection and Analysis of Emotion from Speech Signals. / Davletcharova, Assel; Sugathan, Sherin; Abraham, Bibia; James, Alex Pappachen.
In: Unknown Journal, Vol. 58, 2015, p. 91-96.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Detection and Analysis of Emotion from Speech Signals
AU - Davletcharova, Assel
AU - Sugathan, Sherin
AU - Abraham, Bibia
AU - James, Alex Pappachen
PY - 2015
Y1 - 2015
N2 - Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.
AB - Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.
KW - Emotion Analysis
KW - Emotion Classification
KW - Mel-Frequency Cepstral Coefficients
KW - Speech Processing
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UR - http://www.scopus.com/inward/citedby.url?scp=84970978774&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2015.08.032
DO - 10.1016/j.procs.2015.08.032
M3 - Article
VL - 58
SP - 91
EP - 96
JO - Unknown Journal
JF - Unknown Journal
ER -