Speaker recognition phd thesis

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This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre Author: John Nash. This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features. PhD Abstracts Forensic automatic speaker recognition using Bayesian interpretation and statistical compensation for mismatched conditions Anil Alexander Senior Research Engineer Clarifying Technologies Ltd., Unit 31, Ddole Road, Enterprise Park, Llandrindod Wells, Powys, LD1 6DF, UK [email protected]

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PhD Abstracts Forensic automatic speaker recognition using Bayesian interpretation and statistical compensation for mismatched conditions Anil Alexander Senior Research Engineer Clarifying Technologies Ltd., Unit 31, Ddole Road, Enterprise Park, Llandrindod Wells, Powys, LD1 6DF, UK [email protected] This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre Author: John Nash. This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features.

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This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features. PhD Abstracts Forensic automatic speaker recognition using Bayesian interpretation and statistical compensation for mismatched conditions Anil Alexander Senior Research Engineer Clarifying Technologies Ltd., Unit 31, Ddole Road, Enterprise Park, Llandrindod Wells, Powys, LD1 6DF, UK [email protected] PhD thesis, Queensland University of Technology. The performance of forensic speaker recognition systems degrades significantly in the presence of environmental noise and reverberant blogger.com by: 1.

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This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features. This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre Author: John Nash. PhD thesis, Queensland University of Technology. The performance of forensic speaker recognition systems degrades significantly in the presence of environmental noise and reverberant blogger.com by: 1.

Forensic speaker recognition under adverse conditions | QUT ePrints
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Description

This code is written in MATLAB a version for speaker recognition using LPC and MFCC features. Results of recognition accuracy by both features set are compared and it is analyzed that MFCC features perform well for speaker recognition. Radial Basis Function in a neural network is used to classify those features. PhD Abstracts Forensic automatic speaker recognition using Bayesian interpretation and statistical compensation for mismatched conditions Anil Alexander Senior Research Engineer Clarifying Technologies Ltd., Unit 31, Ddole Road, Enterprise Park, Llandrindod Wells, Powys, LD1 6DF, UK [email protected] This is to certify that the thesis titled, “Study of Speaker Recognition Systems” submitted by Ashish Kumar Panda (EC) and Amit Kumar Sahoo (EC) in partial fulfilments for the requirements for the award of Bachelor of Technology Degree in Electronics and.