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Copy of Copy of Copy of Final Year Project: Voice Recognition system using MATLAB

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hasan alii

on 29 May 2013

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Transcript of Copy of Copy of Copy of Final Year Project: Voice Recognition system using MATLAB

By : Hasan M.Ali Hamedan (M021020009) Developing feature extraction algorithm for automatic voice detection and identification INTRODUCTION RESULT AND ANALYSIS by :
Yusherizan Marshella Binti Yusoh
(EE084076) Literature Review Steps to construct the Voice Recognition System:

Prepare a speech database for training and testing.

Voice processing.

Training speech database.

Simulation and evaluation. RESULT AND ANALYSIS OBJECTIVES To evaluate the performance of different Technique (Mel-Frequency Cepstral Coefficients (MFCCs),perceptual linear prediction Coefficients (PLP) and linear prediction Coefficients (LPC) of feature extraction algorithm for automatic voice detection and identification.

Design Voice identification model that is capable of identifying the trained subject from not trained.

Design Voice verification model that could accept or reject the trained subject from not trained subject accurately.

Implement the design models using Matlab software Voice Database for Training and Testing Create voice database for references to make comparison later on.

Voice database recorded using Microsoft WAV format.

Create database for the TRAIN folder and TEST folder.

The voice model will be trained in (MFCC,PLP and LPC) vector space for each speaker.

After the training steps, system should be able to acknowledge the voice characteristic for each speaker.

Testing phase, system will identify the speaker of each sound file in TEST folder. Voice Processing Voice Processing The speech signal(vector) will be cut into frames. Result of cutting will be a matrix of N samples from original voice.

Applying windowing and FFT to transform the signal into frequency domain (spectrum or periodogram)

Compute power spectrum using imagesc command.

Converting power spectrum into MFCC. Voice Processing Experimental results SIMULATION AND EVALUATION Simulate the training and testing procedure for the voice recognition system. Technique chosen for Feature Extraction and Feature Matching Feature Extraction:
Mel Frequency Ceptrum Coefficient (MFCC)
Perceptual linear Prediction Coefficients (PLP)
Linear Prediction Coefficients (LPC)

Feature Matching:
Gaussian mixture model (GMM) METHODOLOGY Voice Recognition System: Process of automatically recognizing who is the spaeker based on the unique characteristics contained in speech waves.

System implemented as security features for:
-Building acces
-Credit card Verification

Two elemets in Voice Recognition System:
- Feature Extraction: Process of extracting
unique information from speech files.
- Feature Matching: Process of identifying the
speaker that involves comparing unknown
-Literature Review


Faculty of Electronics and Computer Engineering (FKEKK) Literature Review
previous work Voice identification Voice verification Robust voice detection and identification system should be effective under full variation in: environmental conditions, speaker variability.

Heavily speaker dependent

Continuous speech recognition.

Reduce computational time for voice detection and identification algorithm.

Problem staement Introduction Multimedia (audio-visual contents) associated with speech is continuously growing and filling our computers, networks and lives
Such as broadcast news, lectures, shows, voice mails, (contact-center) conversations, etc.

On the other hand, speech is the primary and the most convenient means of communication between people
Speech provides a better (or natural) user interface in wireless environments and especially on smaller hand-held devices.

Speech will be the key for multimedia information access in the near future .

The human speech contain numerous discriminative features that can be used to identify speakers.
CONCLUSION Improvement can be implement in the Voice Recognition System according to the application.

The experimental results indicates that the PLP feature extraction has the best accuracy comparing to the other approaches (MFCC and LPC).

Application that utilize the Voice recognition system:
-Time and attendance system
- Home Automation
- For disabilities people
[1] M.A.Anusuya and S.K.Katti “ Speech Recognition by Machine: A Review” International Journal of Computer Science and Information Security, Vol. 6 ( 3) 2009

[2] Tsang-Long Pao, Wen-Yuan Liao, Tsan-Nung Wu, Ching-Yi Lin “ Automatic Visual Feature Extraction for Mandarin Audio-Visual Speech Recognition’ Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009.

[3] Santosh K.Gaikwad, Bharti W.Gawali and Pravin Yannawar, “A Review on Speech Recognition Technique”, International Journal of Computer Applications (0975 – 8887) Volume 10– No.3, November 2010

[4] Vimala.C and V. Radha “A Review on Speech Recognition Challenges and Approaches” World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 2, No. 1, 1-7, 2012
[5] Wiqas Ghai Khalsa College (ASR) of Technology & Business Studies, Mohali, Punjab Navdeep Singh Mata Gujri College, Fatehgarh Sahib, Punjab
Literature Review on Automatic Speech Recognition Wiqas Ghai , International Journal of Computer Applications (0975 – 8887) Volume 41– No.8, March 2012

Refrences [5] [2] [4]
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