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Introduction to Speech Processing: Concepts & Applications

Join this core module to explore speech production, recognition systems, HMMs, DSP, and ASR. Enhance your communication and programming skills while delving into machine-learning systems.

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Introduction to Speech Processing: Concepts & Applications

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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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