Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim

A sound retrieval method enables users to easily obtain their preferred sound. When we communicate, we exchange the expressive and related messages. This project reviews about identification of expressive speech video segment using acoustic features. Specifically, the segmented expressive speech ret...

Full description

Saved in:

Bibliographic Details
Main Author:	Alim, Nur Amanini Syahirah
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Electronic Computers Computer Science
Online Access:	https://ir.uitm.edu.my/id/eprint/98097/1/98097.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.98097
record_format	uketd_dc
spelling	my-uitm-ir.980972024-08-21T23:27:46Z Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim 2017 Alim, Nur Amanini Syahirah Electronic Computers. Computer Science A sound retrieval method enables users to easily obtain their preferred sound. When we communicate, we exchange the expressive and related messages. This project reviews about identification of expressive speech video segment using acoustic features. Specifically, the segmented expressive speech retrieves the expressive speech and non-expressive speech from the video. From the sermon video that we have choose, the expression of motivator looks like similar from the beginning until the end. The audience cannot focus on what the motivator is talk about because there is no interesting part based on the motivator’s expression. This project applies manual video segmentation to differentiate expressive speech and non-expressive speech. Then, this project extracted the audio features from segmented expressive and non-expressive speech such as pitch and intensity by using Pratt tools. Then, we used Random Forest Classifier technique in Spyder (IDE) using Python language to get the accuracy which is 43% and used the prediction method to classify the expressive speech and non-expressive speech as the intended results. The training audio features was trained to get the performance accuracy. The correctness of the project has been showed from the evaluation. The project compared the predicted and manually segmented data to get the percentage of matches using pitch, the percentage of match is 80% while using the intensity is 75%. The correctness of the results has been verified to improve the identification of expressive speech video segment automatically. 2017 Thesis https://ir.uitm.edu.my/id/eprint/98097/ https://ir.uitm.edu.my/id/eprint/98097/1/98097.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Mohamed Hanum, Haslizatul Fairuz
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohamed Hanum, Haslizatul Fairuz
topic	Electronic Computers Computer Science
spellingShingle	Electronic Computers Computer Science Alim, Nur Amanini Syahirah Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim
description	A sound retrieval method enables users to easily obtain their preferred sound. When we communicate, we exchange the expressive and related messages. This project reviews about identification of expressive speech video segment using acoustic features. Specifically, the segmented expressive speech retrieves the expressive speech and non-expressive speech from the video. From the sermon video that we have choose, the expression of motivator looks like similar from the beginning until the end. The audience cannot focus on what the motivator is talk about because there is no interesting part based on the motivator’s expression. This project applies manual video segmentation to differentiate expressive speech and non-expressive speech. Then, this project extracted the audio features from segmented expressive and non-expressive speech such as pitch and intensity by using Pratt tools. Then, we used Random Forest Classifier technique in Spyder (IDE) using Python language to get the accuracy which is 43% and used the prediction method to classify the expressive speech and non-expressive speech as the intended results. The training audio features was trained to get the performance accuracy. The correctness of the project has been showed from the evaluation. The project compared the predicted and manually segmented data to get the percentage of matches using pitch, the percentage of match is 80% while using the intensity is 75%. The correctness of the results has been verified to improve the identification of expressive speech video segment automatically.
format	Thesis
qualification_level	Bachelor degree
author	Alim, Nur Amanini Syahirah
author_facet	Alim, Nur Amanini Syahirah
author_sort	Alim, Nur Amanini Syahirah
title	Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim
title_short	Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim
title_full	Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim
title_fullStr	Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim
title_full_unstemmed	Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim
title_sort	identification of expressive speech video segment using acoustic features / nur amanini syahirah alim
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/98097/1/98097.pdf
_version_	1811768888382193664

Identification of expressive speech video segment using acoustic features / Nur Amanini Syahirah Alim

Similar Items