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DS1TOP - Techniques for Speech Processing and Recognition

Course specification
Course title Techniques for Speech Processing and Recognition
Acronym DS1TOP
Study programme Electrical Engineering and Computing
Module System Control and Signal Processing
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition non
      The goal Objective of the course is for the students to become capable of applying complex techniques for digital processing and speech recognition.
      The outcome Learning outcomes should be for the students to gain the following skills: endpoints detection in the recorded speech sequence, estimation of pitch frequency, design of different speech quantizers, design of appropriate base of training sequences, design of speech recognition systems based on spectral, cepstral coefficients or hidden Markov chains or artificial neural networks.
      Contents
      Contents of lectures Overview of different systems for processing, memorizing and recognition of speech; Modelling of acoustic waveform, Model of uniform tube, Time-domain speech signal processing, Different techniques of quantizers, Evaluation of quantizers, Homomorphic processing of speech signal, LPC analysis of speech signals, Hidden Markov chains and application in speech recognition.
      Contents of exercises Within the course, students have obligation to solve three practical problems: 1. estimation of their pitch frequency and segmentation of their speech, 2. modelling and evaluation of particular quantizer, 3. design of a hidden Markov chain.
      Literature
      1. Introduction to digital speech processing, L. Rabiner, D. Schafer, now Publishers Inc., 2007. (Original title)
      2. Statistical Methods for Speech Recognition, F. Jelinek, MIT Press, 1997. (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      6
      Methods of teaching lectures
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures 0 Test paper
      Practical lessons 30 Oral examination 70
      Projects 0
      Colloquia 0
      Seminars 0