1. 1 CA461 Speech Processing 1 John McKenna

2. 2 Welcome Admin –Contact –Prerequisites –Assessment Module Overview –Syllabus –Learning Outcomes Introductory Lecture

3. 3 Welcome CA4 students welcome from all streams CL4 core module CLX welcome too –Please mail me if you have doubts about prerequisite knowledge

4. 4 Contact Details Email –john@computing.dcu.iejohn@computing.dcu.ie –John.McKenna@computing.dcu.ieJohn.McKenna@computing.dcu.ie –John.McKenna@dcu.ieJohn.McKenna@dcu.ie Office –Room L2.47 –Tel. (700)5507

5. 5 Logistics Lectures –Twice a week Labs –1 x 2 hour lab per week (start Week 1) Moodle –moodle.dcu.iemoodle.dcu.ie –VLE –Lecture notes, Discussion forums, etc

6. 6 Prerequisites 1.Open mind 2.Some maths –probability, linear algebra (matrices) 3.Ability to program 4.Problem solving skills 5.Communication skills

7. 7 Assessment Continuous Assessment: 60% –1 Assignment: 50% Issued about week 7; due week 12 4-page, conference-style paper on a speech/speaker recognition implementation –APC: 10% End of module exam: 40%

8. 8 APC Not a distance education module Attendance Performance Contribution

9. 9 You will do well in this module if: You think analytically Think for yourself Engage the subject Communicate well

10. 10 Materials Books –See Module Descriptor for list –No book purchase necessary –Recommended Gold & Morgan, or Holmes & Holmes Headset required –Composite (with microphone) recommended –Sharing feasible

11. 11 General –To present the characteristics of speech –To discuss automatic speech recognition systems Specific –Speech Production, Representations and Terminology –Acoustic Phonetics –Overview of ASR (Automatic Speech Recognition) –Speech Parameterisation for ASR –HMMs and Trellis Algorithms –HMM Recognition and Training –Other issues and applications Indicative Syllabus

12. 12 Familiarity with the building blocks of language Understanding of time/frequency representations & DSP Knowledge of pattern matching algorithms Ability to program MATLAB scripts Ability to use HTK (Hidden Markov Model Toolkit) Knowledge of the principles and problems in the design, implementation and evaluation of machine-learning systems Extensible Learning Outcomes

13. 13 Focus on –Speech Analysis –Speech Synthesis Prerequisites –Speech Processing 1 or –possibly DSP 1 Semester 1 –You can choose both DSP1 and SP1 Speech Processing 2?

14. 14 Next… Try the first Lab –Recording –Transcription vs. Orthography –Analysis –Synthesis Next Lecture –Sounds & Speech Production