Comparative analysis of two different system's framework for text dependent speaker verification

Choudhury, Suman Paul; Das, Tushar Kanti; Saha, Partha; Hussain, Rabul; Baruah, Ujwala

doi:10.1109/iccpct.2015.7159435

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Performance of the TD-SV model using MFCC and RMFCC features is measured in terms of EER. The point where false acceptance rate (FAR) and false rejection rate (FRR) intersect each other is the EER [7]. Based on the claim list, DTW scores are obtained by four cohort speaker method using MFCC and RMFCC features.…”

Section: F Performance Evaluationmentioning

confidence: 99%

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Significance of Source Information for Text Dependent Speaker Verification

Hore¹,

Nirmala²,

Das³

et al. 2018

IJIGSP

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This work focuses on text dependent speaker verification system where a source feature specifically residual Mel frequency cepstral coefficients (RMFCC), has been extracted in addition to a vocal tract system feature namely Mel frequency cepstral coefficients (MFCC). The RMFCC features are derived from the LP residuals whereas MFCC features are derived from the cepstral analysis of the speech signal. Thus, these two features have different information about the speaker. A four cohort speaker's set has been prepared using these two features and dynamic time warping (DTW) is used as the classifier. Performance comparison of the text dependent speaker verification model using MFCC and RMFCC features are enumerated. Experimental results shows that, using RMFCC feature alone do not give satisfactory results in comparison to MFCC. Also, the system's performance obtained using the MFCC features, is not optimum. So, to improve the performance of the system, these two features are combined together using different combination algorithms. The proposed lowest ranking method yields good performance with an equal error rate (EER) of 7.50%. To further improve the efficiency of the system, the proposed method is combined along with the strength voting and weighted ranking method in the hierarchical combination method to obtain an EER of 3.75%.

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Section: F Performance Evaluationmentioning

confidence: 99%

“…Different methodologies have been adopted for TD-SV systems, as can be seen from the literature. Several features like MFCC [1], [5], [7], [21], [22] pitch [8], linear prediction coefficients (LPC) [9], perpetual linear prediction coefficients (PLP) [9], [10] etc. are extracted from the speech signal.…”

Section: Introductionmentioning

confidence: 99%

Significance of Source Information for Text Dependent Speaker Verification

Hore¹,

Nirmala²,

Das³

et al. 2018

IJIGSP

View full text Add to dashboard Cite

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Voice authentication by text dependent single utterance for in-car environment

Tamoto

Itou

2019

Proceedings of the Tenth International Symposium on Information and Communication Technology - SoICT 2019

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Voice Classification Based on Fast Independent Component Analysis to Support Nuclear Power Plant Security

Budi

Suparman

2020

Atom Indo.

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National vital objects have important role in national development, so they require special protection. Nuclear power plant (NPP) is one of them. Access restriction is required to prevent the NPP from potential hazards. The restriction can be improved by using face, fingerprints, retina, iris, and voice password. The improvement will enhance the security of the NPP. This research has implemented pattern recognition and classification of voice passwords. The passwords were a, i, u, e, and o. The features vector was searched by using Fast ICA method while the pattern classification was performed by minimum Euclidean method. The purpose of this research is to recognize and classify those letter password, so people who have access to the nuclear area can be distinguished. The methodology of this research consists of input data, pre-processing data, feature extraction, and classification. Pre-processing was done by normalization, denoising, centering, and whitening. Feature extraction was performed by Fast ICA method, and classification was done by minimum Euclidean distance. The results show that Fast ICA and minimum Euclidean methods can 100 % distinguish between the employees who have access permit and those who have no access permit. When an employee with access permit says "aiueo", it will be recognized as password, whereas when an employee with no access permit says the password, it will be recognized not as the password.

show abstract

Comparative analysis of two different system's framework for text dependent speaker verification

Cited by 3 publications

References 11 publications

Significance of Source Information for Text Dependent Speaker Verification

Significance of Source Information for Text Dependent Speaker Verification

Voice authentication by text dependent single utterance for in-car environment

Voice Classification Based on Fast Independent Component Analysis to Support Nuclear Power Plant Security

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