An HMM approach to text-prompted speaker verification

Che, ChiWei; Lin, Qing; Yuk, Dong-Suk

doi:10.1109/icassp.1996.543210

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…Text prompted: In text prompted, speaker verification system will select a random word, read it to the called and ask the caller to repeat it exactly [22].…”

Section: Classification Of Speaker Recognitionmentioning

confidence: 99%

Adaptive Speech Analysis Techniques for Biometrics Applications

Mozaffar¹

2021

Preprint

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Using biometrics for personal identity authentication is becoming more reliable and accurate than current methods because it links the event to a particular individual (e.g. a password may be used by someone other that the authorized user), is convenient (nothing to carry or remember), accurate (provides positive authentication) and is socially acceptable. Adaptive frequency sub-band or time frame recombination approaches are introduced in this thesis.

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“…Text prompted: In text prompted, speaker verification system will select a random word, read it to the called and ask the caller to repeat it exactly [22].…”

Section: Classification Of Speaker Recognitionmentioning

confidence: 99%

Adaptive Speech Analysis Techniques for Biometrics Applications

Mozaffar¹

2021

Preprint

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“…Campbell described the bias dilemma, hypothesizing that reported results will be biased low (decreased False Acceptance rates) when either excluding or allowing cohort speakers. Cohort speakers normalize the test score to remove various word effects of the utterance and have been shown to greatly improve verification accuracy [2]. This article first examines an alternative testing methodology to remove this bias from the reported %ual %or Rate (EER) or Zero Rejection Rate (ZRR) performance of a hidden Markov model speaker verification system.…”

Section: Introductionmentioning

confidence: 99%

Allowing good impostors to test

Colombi

Reider²,

Campbell³

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)

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Biometric testing should attempt to report unbiased, real-world system performance, especially when tested on limited databases. Though testing on a standard database, such as the Linguistic Data Consortiums's YOHO, allows comparison of speaker ver@cation systems, it is well known that certain procedures bias the results low. One such procedure concerns the use of cohort or reference speakers to pe'form veriJication, where the cohort speakers are removed as candidate impostors. A method of testing is proposed to remove this bias by modifying the cohort set for each false acceptance test. Results statistically differ for this mod$ed approach, which tries to "best" model the general population with a fixed random sample. Lastly, three techniques to bound the biometric perjbrmance, using both parametric and noli-parametric resampling is demonstrated.

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