Reading
Jurafsky & Martin – Section 9.2 – The HMM Applied to Speech
Introduces some notation and the basic concepts of HMMs.
Jurafsky & Martin – Section 9.4 – Acoustic Likelihood Computation
To perform speech recognition with HMMs involves calculating the likelihood that each model emitted the observed speech. You can skip 9.4.1 Vector Quantization.
Young et al: Token Passing
My favourite way of understanding how the Viterbi algorithm is applied to HMMs. Can also be helpful in understanding search for unit selection speech synthesis.
Sharon Goldwater: Basic probability theory
An essential primer on this topic. You should consider this reading ESSENTIAL if you haven't studied probability before or it's been a while. We're adding this the readings in Module 7 to give you some time to look at it before we really need it in Module 9 - mostly we need the concepts of conditional probability and conditional independence.
Holmes & Holmes – Chapter 9 – Stochastic Modelling
May be helpful as a complement to the essential readings.
Jurafsky & Martin (3rd Ed) – Hidden Markov models
An overview of Hidden Markov Models, the Viterbi algorithm, and the Baum-Welch algorithm