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Training on
The Hidden MARKOV Model
with application in Speech Analysis and Signal Recognition

Klang Valley
Write in to Confirm
"a series of hands-on exercises which help in translating
HMM theoretical models to practical applications ..."

Speech recognition is the process by which a computer or machine identifies spoken words. The speech recognition system, in general, comprises speech segmentation, feature extraction, and feature matching with a trained library of stored features. Speech segmentation is easily accomplished by segmenting at points where the power of the sampled signal goes to zero. Feature extraction may be done in a variety of ways, depending on the features one chooses to extract. Typically, these are the coefficients that collectively represent the short-time spectrum of the speech signal, such as the mel-frequency cepstral coefficients (MFCCs) or the linear prediction cepstral coefficients (LPCCs). Feature matching is traditionally implemented via dynamic time warping, which provides a means for the temporal alignment of two speech signals which may vary in time or speed.


Modern speech recognition systems are, however, based on hidden Markov models (HMMs), developed by Leonard E. Baum and his coworkers in the late 1960s. The HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (or hidden) states. As speech signals are short-time stationary processes, modeling speech signals as HMMs is feasible and offers great advantages over the predecessor.

This course is conducted in a workshop-like manner, with a balance mix of theory and hands-on coding and simulation in MATLAB. Extensive exercises are provided throughout the course to cover every angle of algorithm design and implementation using MATLAB.

The course begins with an overview of the speech recognition problem, and a review of some common speech analysis models. The dynamic time warping algorithm and the hidden Markov model are then introduced in turn, with the basic principles behind these methods discussed both through theory and practice. Programming examples are provided at the end of each section to help reconcile theory with actual application.

Course Outline

Day 1

  • Introduction
  • The speech signal
  • The signal classification problem
  • Speech analysis models
    Filter banks, Critical band scales, Linear prediction, Autoregressive models, Homomorphic systems, Cepstral transformation, Cepstral coefficients
  • Pattern recognition
    Distance and distortion measures, Time alignment, Dynamic time warping, Dynamic programming

Day 2

  • Hidden Markov models
    States and observations, State transition probabilities and observation probabilities, The three problems
  • The evaluation problem
    Forward and backward variables
  • The decoding problem
    Viterbi algorithm
  • The learning problem
    Maximum likelihood estimation, Expectation-maximization algorithm, Discrete observation symbols, Continuous observation densities
  • Implementation issues
    Left-right hidden Markov models, The initial estimates

Who Should Attend
The primary target audiences for this course are scientist, mathematicians, engineers and programmers at all levels who work with or need to learn about speech recognition and/or the hidden Markov model. No background in either of these topics are, however, assumed. The detailed course material and many source code listings will be invaluable for both learning and reference.

A basic knowledge of probability theory, signal processing and Matlab programming is necessary.

Note: Due to the nature of the course and the learning expectations, the availability seats are limited. You need to register early to obtain confirmation of your space.

02 & 03 OCT, 2017 @ MY

Venue : Klang Valley
10 % Early Registration Discount before 02 SEP 2017
10 % Group Discount for 3 or more from same organization

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