This document discusses speaker identification and authentication methods. It presents an overview of authentication approaches like tokens, knowledge, and biometrics. It then reviews literature on speech recognition paradigms and approaches. The methodology section proposes a speaker identification system that preprocesses voice files, extracts LPC features, and uses an artificial neural network for pattern matching. Results show the system achieves around 84% correct identification accuracy on 88 samples. Recommendations include interconnecting LPC and neural network modules, enabling text-independent identification, increasing the number of users and accuracy, and enabling cross-platform development.

1. .
Aththnagoda A.K.N.L
PGIS/Msc/cs/11/06
Post Graduate Institute Of Science
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2. Content
 Introduction
 Literature Review
 Methodology
 Implementation
 Results and discussion
 Recommendation
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3. Introduction
 Authentication
The process of confirming an individual’s identity,
either by verification or identification.
 This may be carried out by;
 A person recognizing a person
 Access control (PC, ATM, mobile phone)
 Physical access control (house, building, area)
 Identification (passport, driving license)
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4. 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)
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5. Literature Review
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Classification paradigms used in SRS

6. Approaches to speech recognition
According to the literature speech recondition
approaches can be categorize into two main
different approaches.
 The Artificial Intelligence approach
 The Pattern Recognition approach
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7. Methodology
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8. Proposed Speaker Identification
System
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9. 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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10. Feature extraction of voice file
 It can be use many methods to feature
extraction of a voice file
 Dynamic information
 Log energy and ∆ log energy
 Discarding useless information
 Filter bank-based cepstral parameters
 Linear Predictive Coding
 Centered and reduced vectors
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11. 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 speech
Voice/Unvoiced Determination
Pitch Period Estimation
LPC Synthesis/Decoding
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12. 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 patterns
 Recognition of patterns
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13. Artificial neural network for
pattern matching
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• Back propagation algorithm is used to
implement the ANN

14. Implementation
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15. Results
 LPC module
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16. Results con’t
 The correct speaker identification accuracy
is around 84% for the proposed system
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No of samples 88
Correct Identification 74
False Rejection 8
False Acceptance 6

17. 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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18. 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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19. 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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20. Thank you.
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