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CA461 Speech Processing 1

CA461 Speech Processing 1. John McKenna. Introductory Lecture. Welcome Admin Contact Prerequisites Assessment Module Overview Syllabus Learning Outcomes. Welcome. CA4 students welcome from all streams CL4 core module CLX welcome too

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CA461 Speech Processing 1

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  1. CA461 Speech Processing 1 John McKenna

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

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

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

  6. Prerequisites • Open mind • Some maths • probability, linear algebra (matrices) • Ability to program • Problem solving skills • Communication skills

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

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

  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. Indicative Syllabus • 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

  12. Extensible Learning Outcomes • 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

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

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

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