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dc.contributor.authorRaman, Pujitaen_US
dc.date.accessioned2015-06-18T08:02:27Z
dc.date.available2015-06-18T08:02:27Z
dc.date.issued2015-06-17en_US
dc.identifier.othervt_gsexam:5735en_US
dc.identifier.urihttp://hdl.handle.net/10919/52964
dc.description.abstractState-of-the-art speaker identification and verification (SIV) systems provide near perfect performance under clean conditions. However, their performance deteriorates in the presence of background noise. Many feature compensation, model compensation and signal enhancement techniques have been proposed to improve the noise-robustness of SIV systems. Most of these techniques require extensive training, are computationally expensive or make assumptions about the noise characteristics. There has not been much focus on analyzing the relative importance, or speaker-discriminative power of different speech zones, particularly under noisy conditions. In this work, an automatic, text-independent speaker identification (SI) system and speaker verification (SV) system is proposed using Line Spectral Frequency (LSF) features. The performance of the proposed SI and SV systems are evaluated under various types of background noise. A score-level fusion based technique is implemented to extract complementary information from static and dynamic LSF features. The proposed score-level fusion based SI and SV systems are found to be more robust under noisy conditions. In addition, we investigate the speaker-discriminative power of different speech zones such as vowels, non-vowels and transitions. Rapidly varying regions of speech such as consonant-vowel transitions are found to be most speaker-discriminative in high SNR conditions. Steady, high-energy vowel regions are robust against noise and are hence most speaker-discriminative in low SNR conditions. We show that selectively utilizing features from a combination of transition and steady vowel zones further improves the performance of the score-level fusion based SI and SV systems under noisy conditions.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectSpeechen_US
dc.subjectSpeakeren_US
dc.subjectNoiseen_US
dc.subjectIdentificationen_US
dc.subjectVerificationen_US
dc.subjectRecognitionen_US
dc.subjectFeatureen_US
dc.subjectLine Spectral Frequencyen_US
dc.subjectGaussian Mixture Modelen_US
dc.subjectTransitionen_US
dc.subjectVowelen_US
dc.titleSpeaker Identification and Verification Using Line Spectral Frequenciesen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.committeechairBeex, Aloysius A.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.contributor.committeememberYu, Guoqiangen_US


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