vret - User contributions [en-gb]

Measuring fear from voice

2009-08-24T18:36:38Z

Ifa Chaeron: /* Project */

S.L.A. Chaeron

Human Computer Interaction

Graduated: 2009

== Project ==
Thesis title: Ranking the Level of Fear from Voice using Nominal Classification Methods

=== 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

2009-08-24T12:17:34Z

Ifa Chaeron:

S.L.A. Chaeron

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

2009-08-24T12:11:04Z

Ifa Chaeron:

S.L.A. Chaeron

Human Computer Interaction