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Using Gaussian Mixture Model And Partial Least Squares Regression Classifiers For Robust Speaker Verification With Various Enhancement Methods, Joshua Scott Edwards
Using Gaussian Mixture Model And Partial Least Squares Regression Classifiers For Robust Speaker Verification With Various Enhancement Methods, Joshua Scott Edwards
Theses and Dissertations
In the presence of environmental noise, speaker verification systems inevitably see a decrease in performance. This thesis proposes the use of two parallel classifiers with several enhancement methods in order to improve the performance of the speaker verification system when noisy speech signals are used for authentication. Both classifiers are shown to receive statistically significant performance gains when signal-to-noise ratio estimation, affine transforms, and score-level fusion of features are all applied. These enhancement methods are validated in a large range of test conditions, from perfectly clean speech all the way down to speech where the noise is equally as loud …
Robust Speaker Recognition In The Presence Of Speech Coding Distortion, Robert Walter Mudrosky
Robust Speaker Recognition In The Presence Of Speech Coding Distortion, Robert Walter Mudrosky
Theses and Dissertations
For wireless remote access security, forensics, border control and surveillance applications, there is an emerging need for biometric speaker recognition systems to be robust to speech coding distortion. This thesis examines the robustness issue for three coders, namely, the ITU-T 6.3 kilobits per second (kbps) G.723.1, the ITU-T 8 kbps G.729 and the 12.2 kbps 3GPP GSM-AMR coder. Both speaker identification (SI) and speaker verification (SV) systems are considered and use a Gaussian mixture model (GMM) classifier. The systems are trained on clean speech and tested on the decoded speech. To mitigate the performance loss due to mismatched training and …