Speaker Verification Is A Biometric Authentication Model That Takes SpeechrnSignal As An Input To Verify A Claimed Speaker. A Speaker Verification ModelrnExtracts Speaker Dependent Characteristics From The Speech Wave Signal So As TornCreate The Voiceprint Of The Speaker. The Researcher Has Implemented The Logic InrnAn English Speaker Verification Model [45] For Amharic. But The Average AccuracyrnObtained For Ten Amharic Words Is 92.93%. The Research Work Is Initiated FromrnThis Implementation And Its Subsequent Poor Accuracy Performance. In This ThesisrnResearch Work, Amharic Text-Prompted Speaker Verification Model (ATPSVM) IsrnDesigned And Implemented.rnThe ATPSVM Model Applies Frame-Based Processing To The Speech WavernSignals So That All Samples In A Frame Are Processed Simultaneously. It ExtractsrnSpeaker Feature Vectors As Mel Frequency Cepstral Coefficients For Use In SpeakerrnModel Construction. Then It Applies The Parameter Domain (Spectral) NormalizationrnFollowed By The Min-Max Normalization On The Speaker Feature Vectors So As TornScale The Feature Vector Values In [0, 1]. Finally, It Applies Support Vector MachinernKernel Functions For Modelling Each Speaker. For A Specific Amharic WordrnPrompted, It Utilizes One-Against-Each SVM Speaker Modeling Strategy TornMaintain The Balance Of The Test Speaker Feature Vectors In The Mixed Features.rnThe ATPSVM Model Prototype Is Evaluated Using Ten Amharic Words. EachrnAmharic Word Is Uttered Ten Times Repeatedly By 5 Men And 5 Women. So That ArnTotal Of 100 Speech Wave Files Are Recorded From Each Speaker.rnOne Utterance Is Iteratively Taken For Testing While The Remaining 9 Are UsedrnFor Training The Speaker On Leave-One-Out Basis. It Iteratively Takes OnernUtterance Of Each Speaker Against Other 9 Speakers For Testing. The RespectivernAmharic Text-Prompted Speaker Verification Model Page XviirnUtterance Of Other Speaker Is Taken As Impostor Data Set For The Same. ThernRemaining Respective 9 Utterances From The Two Speakers Are Taken As TrainingrnData Sets.rnThus For Each Amharic Word, The Model Is Evaluated Using 900 Training DatarnSets, 900 Client Testing Data Sets And Another 900 Impostor Testing Data Sets. In Total, ThernATPSVM Model Is Evaluated Using 9,000 Training Data Sets, 9,000 Client (Target) TestingrnData Sets, And Another 9,000 Impostor Testing Data Sets. The Evaluation Of The Model IsrnDone By Varying The Threshold Theta Between 0.0 And 1.0 With 0.1 Differences.rnPerformance Is Measured In Terms Of False Acceptance, False Rejection, TruernAcceptance And True Rejection. Then It Is Reported As Precision, Accuracy, Recall,rnFalse Acceptance Rate, False Rejection Rate And Equal Error Rate (EER). ThernATPSVM Model Is Evaluated Using The SVM Linear, Gaussian Radial Basis Networkrn(RBF), Multilayer Perceptron (MLP), And Polynomial Power 3 Kernel Functions.rnBest Performance Of The ATPSVM Model Is Obtained When The SVM PolynomialrnPower 3 Kernel Function Is Applied. The Performance Difference Between ThernKernel Functions Follows From The Algorithmic Definition Of The Same.rnUsing The SVM Polynomial Power 3 Kernel Function, For The Ten AmharicrnWords Experimented, An Average Performance Of 0.25% EER, 99.7% Accuracy,rn99.8% Recall And 99.7% Precision Is Obtained. For The Same Kernel, The Performance OfrnThe ATPSVM Model For Each Amharic Word Is Also Evaluated Separately. For 70% Of ThernAmharic Words Experimented, The Performance Of The Model Is 0.00% EER With 100%rnAccuracy, 100% Precision, And 100% Recall Values. For The Remaining 30%, ItsrnPerformance Is Slightly Lower Than These Values. By Selecting More DiscriminativernAmharic Words Of Similar Nature To The Seven Words, It Is Possible To Get ThernDesired Highest Performance From The ATPSVM Model.rnKeywords: Speaker Verification, Speaker Recognition, Biometric Authentication,rnAmharic Text-Prompted Speaker Verification, Text-Prompted Speaker Verification