A complete issue of IEEE Signal Processing Magazine. Although a few years old, this is still a very useful survey of current techniques.
Holmes & Holmes – Chapter 11 – Improving Speech Recognition Performance
We mitigate the over-simplifications of the model using ever-more-complex algorithms.
Holmes & Holmes – Chapter 9 – Stochastic Modelling
May be helpful as a complement to the essential readings.
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.
Jurafsky & Martin (3rd Ed) – Hidden Markov models
An overview of Hidden Markov Models, the Viterbi algorithm, and the Baum-Welch algorithm
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.
