1. Supervisor: Dr. Eddie Jones Co-supervisor: Dr Martin Glavin Electronic Engineering Department Final Year Project 2008/09 Development of a Speaker Recognition/Verification System for Security Applications

2. Contents Background Initial objectives Development steps Outcomes/Conclusion Questions

3. Background Universal method of communication. Unique to each user. Speech as a user interface: Telephone banking. Call centre routing.

4. Background What 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.

5. Basic flow diagram of the system

6. Initial Objectives Research 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

7. Development steps Research: Matlab Speaker recognition MFCC (Mel Frequency Cepstral Coefficients) ANN (Artificial Neural Networks) VoIP technology & speaker recognition Front End Processor Classifier

8. Front End Processor Preparing the signal for analysis: Endpointing, framing, windowing, overlapping, analysis using MFCC fitlerbank, timewarping. What are MFCCs: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.

9. Classifier ANN (Artificial Neural Network) - Interconnected group of artificial neurons which processes information using a connectionist approach to information processing. Multilayer Perceptron: 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

10. Outcomes/Conclusion Development of knowledge on Speaker Recognition software. Development of the MATLAB programming language skills. Speaker characteristics extracted from speech.

11. Questions?

12. MFCC Filter bank

13. Artificial Neural Network