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Implementation and Improvement of Co...

Wang, Yunlong.

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Implementation and Improvement of Common Text-Independent Speaker Identification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Implementation and Improvement of Common Text-Independent Speaker Identification./
Author:	Wang, Yunlong.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	68 p.
Notes:	Source: Masters Abstracts International, Volume: 82-04.
Contained By:	Masters Abstracts International82-04.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28025090
ISBN:	9798672185699

Implementation and Improvement of Common Text-Independent Speaker Identification.
Wang, Yunlong.

Implementation and Improvement of Common Text-Independent Speaker Identification. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 68 p.

Source: Masters Abstracts International, Volume: 82-04.

Thesis (M.S.)--Northern Illinois University, 2020.

This item must not be sold to any third party vendors.

The purpose of this thesis is to improve and implement the existing speaker recognition system. The proposed system will be cooperate with automatic speech recognition for automatically generating a script file for a recorded dialog file. Today's speech recognition technology has been widely used in many application areas; however, speaker recognition technology needs to be improved for such a purpose in noisy environment. In the process of a recorded file including multi-person conversation, manual speaker identification is often required. The proposed technique can automatically find the ID of each speaker and save manpower. It has potential applications in many other fileds.This thesis attempts to use active noise cancellation and Wiener filter to enhance the speech signal. Several feature extraction methods, such as short-term autocorrelation function, mel-frequency cepstral coefficient (MFCC) and linear prediction coefficient (LPC) have been used to extract patterns from the enhanced signal. Then the features have been used by classifiers: Gaussian mixture speaker classifier, artificial neural network (ANN) and innovative nearest sliding neighborhood. We use the real data collected from human subjects to test the performance of the proposed system. Wiener filter and iterative adaptive noise cancellation can successfully reduce the ambient noise by 21dB. The experimental results show that the nearest sliding neighborhood algorithm has the highest classification rate of 93.04% for gender recognition and recognition rate of 76.32% for speaker recognition.

ISBN: 9798672185699Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Speaker identification

Implementation and Improvement of Common Text-Independent Speaker Identification.
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245 1 0 $a Implementation and Improvement of Common Text-Independent Speaker Identification.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 68 p.
500 $a Source: Masters Abstracts International, Volume: 82-04.
500 $a Advisor: Liu, Lichuan.
502 $a Thesis (M.S.)--Northern Illinois University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a The purpose of this thesis is to improve and implement the existing speaker recognition system. The proposed system will be cooperate with automatic speech recognition for automatically generating a script file for a recorded dialog file. Today's speech recognition technology has been widely used in many application areas; however, speaker recognition technology needs to be improved for such a purpose in noisy environment. In the process of a recorded file including multi-person conversation, manual speaker identification is often required. The proposed technique can automatically find the ID of each speaker and save manpower. It has potential applications in many other fileds.This thesis attempts to use active noise cancellation and Wiener filter to enhance the speech signal. Several feature extraction methods, such as short-term autocorrelation function, mel-frequency cepstral coefficient (MFCC) and linear prediction coefficient (LPC) have been used to extract patterns from the enhanced signal. Then the features have been used by classifiers: Gaussian mixture speaker classifier, artificial neural network (ANN) and innovative nearest sliding neighborhood. We use the real data collected from human subjects to test the performance of the proposed system. Wiener filter and iterative adaptive noise cancellation can successfully reduce the ambient noise by 21dB. The experimental results show that the nearest sliding neighborhood algorithm has the highest classification rate of 93.04% for gender recognition and recognition rate of 76.32% for speaker recognition.
590 $a School code: 0162.
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650 4 $a Industrial engineering. $3 526216
650 4 $a Remote sensing. $3 535394
650 4 $a Computer science. $3 523869
650 4 $a Artificial intelligence. $3 516317
653 $a Speaker identification
653 $a Speaker recognition system
653 $a Automatic speech recognition
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710 2 $a Northern Illinois University. $b Electrical Engineering. $3 1030613
773 0 $t Masters Abstracts International $g 82-04.
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