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SPEAKER IDENTIFICATION BASED USER AUTHENTICATION SYSTEM

. Aththnagoda A.K.N.L

PGIS/Msc/cs/11/06Post Graduate Institute Of Science

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Content Introduction Literature Review Methodology Implementation Results and discussion Recommendation

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Introduction AuthenticationThe process of confirming an individual’s identity, either by verification or identification. This may be carried out by;

A person recognizing a personAccess control (PC, ATM, mobile phone)Physical access control (house, building, area)Identification (passport, driving license)

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User authentication methods Token – “something that you have” such as smart

card, magnetic card, key, passport, USB token Knowledge – “something that you know” such as

password, PIN Biometrics – “something that you are”

A physiological characteristic (such as fingerprint, iris pattern, form of hand)

A behavioral characteristic (such as the way you sign, the way you speak)

Literature Review

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Classification paradigms used in SRS

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Approaches to speech recognition

According to the literature speech recondition approaches can be categorize into two main different approaches.

The Artificial Intelligence approachThe Pattern Recognition approach

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Methodology

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Proposed Speaker Identification System

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Preprocessing of voice file Preprocessing covers digital filtering and end

point detection. Filtering is to filter out any surrounding noise using several algorithms of digital filtering

In the proposed system preprocessing of the voice has been done by using Daubechies' scaling and wavelet filter

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Feature extraction of voice file It can be use many methods to feature

extraction of a voice fileDynamic informationLog energy and ∆ log energyDiscarding useless informationFilter bank-based cepstral parameters Linear Predictive CodingCentered and reduced vectors

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Feature extraction of voice file The LPC (Linear Predictive Coding) calculates

a logarithmic power spectrum of the voice signal

Structure of the algorithm LPC Analysis/Encoding Input speechVoice/Unvoiced DeterminationPitch Period Estimation LPC Synthesis/Decoding

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Pattern matching The Pattern matching approach to speech is

basically one in which the speech patterns are used directly without explicit feature determination and segmentation.

As in most pattern recognition approaches, the method has two steps Training of speech patternsRecognition of patterns

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Artificial neural network for pattern matching

• Back propagation algorithm is used to implement the ANN

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Implementation

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Results LPC module

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Results con’t The correct speaker identification accuracy

is around 84% for the proposed system

No of samples 88

Correct Identification 74

False Rejection 8

False Acceptance 6

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Conclusion Neural network and LPC techniques have been

used as a hybrid approach for speaker identification, with the intention of that a better performance of identification is to be obtained

The software works fine for identifying speaker negligible, from number of different speakers. But the system has limitation with vocabulary, number of users, and it is works only for ‘.wav ‘files

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Recommendations LPC module and neural network module should

be interconnected Text independent speaker identification

technologies (can only use specific word ‘zero’ only for the current system

Increase the number of users that can be added for the system, and increase the accuracy

Cross platform development

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References Ing. Milan Sigmund, CSc. “Speaker Recognition, Identifying

People by their Voices”, Brno University of Technology, Czech Republic, Habilitation Thesis, 2000.

L. P. Cordella, P. Foggia, C. Sansone, M. Vento, “A Real-Time Text-Independent Speaker Identification System”, Proceedings of the ICIAP, pp. 632, 2003.

D.A. Reynolds, L.P. Heck, “Automatic Speaker Recognition”, AAAS 2000 Meeting, Humans, Computers and Speech Symposium, 19 Feb 2000.

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Thank you.