supervisor: dr. eddie jones co-supervisor: dr martin glavin electronic engineering department final...

Supervisor: Dr. Eddie JonesCo-supervisor: Dr Martin Glavin

Electronic Engineering Department Final Year Project 2008/09

Development of a Speaker Recognition/Verification System

for Security Applications

ContentsBackgroundInitial objectives Development stepsOutcomes/ConclusionQuestions

BackgroundUniversal method of communication.Unique to each user.Speech as a user interface:

Telephone banking.Call centre routing.

BackgroundWhat is Speaker Recognition?

Recognition of who is speaking based on characteristics of their speech signal.

Speaker Identification: Determines which registered speaker has spoken.

Speaker Verification: Accept or reject a claimed identity of a speaker.

Enrolling in the system with speech samples.

Basic flow diagram of the system

Feature/characteristics

Extraction

Similarity

Similarity

Similarity

Maximum Resemblance

selected

Matrix of speaker 2 characteristics

Matrix of speaker 1 characteristics

Matrix of speaker N characteristics

Input Speech Speaker identified

Initial ObjectivesResearch into speaker

recognition/verification.Simulation of Front End Processor in

MATLAB.Simulation of Classifier (Neural

Networks).Investigation of Speaker Recognition

over the internet.Investigation and development of a real-

time version of the system

Development stepsResearch:

MatlabSpeaker recognitionMFCC (Mel Frequency Cepstral

Coefficients)ANN (Artificial Neural Networks)VoIP technology & speaker recognition

Front End ProcessorClassifier

Front End ProcessorPreparing the signal for analysis: Endpointing,

framing, windowing, overlapping, analysis using MFCC fitlerbank, timewarping.

What are MFCCs:Close representation to the human auditory

system.Triangular filters spaced linearly and

logarithmically at low and high frequencies respectively.

Triangular filter are used to weigh a piece of the spectrum, and then the weighted values are summed together to give the overall filter output.

Preparing utterance data for training to the neural network.

ClassifierANN (Artificial Neural Network) -

Interconnected group of artificial neurons which processes information using a connectionist approach to information processing.

Multilayer Perceptron:Input nodes of the database of speakers.Hidden layer to weigh each connection to

show the behaviour of the network.Output node matches to the input data.A high output value will appear on the correct

node

Outcomes/ConclusionDevelopment of knowledge on Speaker

Recognition software.Development of the MATLAB

programming language skills.Speaker characteristics extracted from

speech.

Questions?

MFCC Filter bank

0 20 40 60 80 100 120 1400

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Artificial Neural NetworkHidden Units

Input Units Output units

i

j

k

Connection weights