Ifa Chaeron: /* Project */

2009-08-24T18:36:38Z

‎Project

Ifa Chaeron at 12:17, 24 August 2009

2009-08-24T12:17:34Z

Ifa Chaeron at 12:11, 24 August 2009

2009-08-24T12:11:04Z

New page

S.L.A. Chaeron

Human Computer Interaction

@@ Line 6: / Line 6: @@
 == Project ==
+Thesis title: Ranking the Level of Fear from Voice using Nominal Classification Methods
 === Abstract ===

← Older revision		Revision as of 12:17, 24 August 2009
Line 2:		Line 2:

	Human Computer Interaction		Human Computer Interaction
		+
		+	Graduated: 2009
		+
		+	== Project ==
		+
		+
		+	=== Abstract ===
		+	To investigate human emotion, which is conveyed in human speech, methods which
		+	can achieve this need to be developed. One way to respond to it is to extract prosodic features
		+	from speech which are relevant in emotion research
		+	and feed this to a machine learning algorithm. This thesis discusses the
		+	encoding, decoding and inference processes of emotional sentences. These sentences were simulated by 3 actresses and 4 actors.
		+	Each person simulated 10 sentences with each sentence being performed in three emotional levels (neutral, fearful and very fearful).
		+	In total, the dataset consisted of 208 speech samples. Three decoders, two
		+	psycho-therapist and one speech-language pathologist rated each sample
		+	into the three emotional levels. Out of the three, two were highly correlated
		+	when the samples were presented randomly. Their ratings were used to code the
		+	samples. The formants, F1, F2, F3, F4, F0 (fundamental frequency) and
		+	the intensity were extracted from each sample and were given as input to a machine learning algorithm called
		+	Support Vector Machine (SVM).
		+	Due to the ordinal nature of the samples, SVM was used as the base learner of a
		+	meta classifier called OrdinalClassClassifier which exploits the ordering properties of the attributes. This algorithm served as a classifier analyzing
		+	the data from the samples.
		+	The results showed that the performance of the combination SVM and
		+	OrdinalClassClassifier was not significantly better than when the classifier
		+	SVM was only used. After doing a selection process with six different features,
		+	intensity and F0 were found to be the most relevant.

Measuring fear from voice - Revision history

Ifa Chaeron: /* Project */

Ifa Chaeron at 12:17, 24 August 2009

Ifa Chaeron at 12:11, 24 August 2009