ANNs for Automatic Speech Recognition—A Survey

Jolad, Bhuvaneshwari; Khanai, Rajashri

doi:10.1007/978-981-16-2126-0_4

Cited by 3 publications

(1 citation statement)

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“…In other words, as the threshold rises, so do the number of bogus acceptances and denials. FA and FR mistakes are typically balanced based on a threshold value [23].…”

Section: Proposed Methods In Asv System Dependent On Textmentioning

confidence: 99%

Enhancement and modification of automatic speaker verification by utilizing hidden Markov model

Kadhim

Nasret²,

Mahmood³

2022

IJEECS

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<div class="WordSection1"><p>The purpose of this study is to discuss the design and implementation of autonomous surface vehicle (ASV) systems. There’s a lot riding on the advancement and improvement of ASV applications, especially given the benefits they provide over other biometric approaches. Modern speaker recognition systems rely on statistical models like hidden Markov model (HMM), support vector machine (SVM), artificial neural networks (ANN), generalized method of moments (GMM), and combined models to identify speakers. Using a French dataset, this study investigates the effectiveness of prompted te xt speaker verification. At a context-free, single mixed mono phony level, this study has been constructing a continuous speech system based on HMM. After that, suitable voice data is used to build the client and world models. In order to verify speakers, the text-dependent speaker ver-ification system uses sentence HMM that have been concatenated for the key text. Normalized log-likelihood is determined from client model forced by Viterbi algorithm and world model, in the verification step as the difference between the log-likelihood. At long last, a method for figuring out the verification results is revealed.</p></div>

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“…In other words, as the threshold rises, so do the number of bogus acceptances and denials. FA and FR mistakes are typically balanced based on a threshold value [23].…”

Section: Proposed Methods In Asv System Dependent On Textmentioning

confidence: 99%