Speech recognition to determine emotions using quick propagation neural network / Aisyah Mohamad Tojid

Mohamad Tojid, Aisyah (2006) Speech recognition to determine emotions using quick propagation neural network / Aisyah Mohamad Tojid. Degree thesis, Universiti Teknologi MARA.

Download

[thumbnail of TB_AISYAH MOHAMAD TOJID CS 06_5 P01.pdf] Text
TB_AISYAH MOHAMAD TOJID CS 06_5 P01.pdf

Download (83kB)

Abstract

Emotions play important roles in expressing feelings as it tend to make people acts differently. Determine emotions of other people are less complicated if we are facing
each other rather than from voice independently such as conversation in telephone. A main industry that major dealing with telephone as a medium for services is call center. Thus it is a significant step to developing a prototype system for this industry. This project is focusing on speech recognition to determine emotions in call center
environment. The objectives of this project are to identify the quick propagation neural network, determine the emotion through the recorded speech and develop the prototype system. This system will implement the quick propagation neural network using 65 of speech signal as a sample data. Two features will be extracted from each speech signal which are the Fundamental Track Frequency (FTT) and Mel Frequency Ceptral Coefficient (MFCC). It covers types of emotions which are happy, sad and anger
emotions state. However to ensure the ability of the prototype, few experiments are being conducted to achieved the satisfy values for parameter for the prototype inputs to achieve the efficiency. In a conclusion, the prototype is able to determine emotions states from
voice using Quick Propagation neural network.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email
Mohamad Tojid, Aisyah
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Computer software
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Item ID: 1365
URI: https://ir.uitm.edu.my/id/eprint/1365

Fulltext

Fulltext is available at:
  • UNSPECIFIED
  • ID Number

    1365

    Indexing


    View in Google Scholar

    Edit Item
    Edit Item