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.
Sharon Goldwater: Vectors and their uses
A nice, self-contained introduction to vectors and why they are a useful mathematical concept. You should consider this reading ESSENTIAL if you haven’t studied vectors before (or it’s been a while).
Jurafsky & Martin – Section 9.1 – Speech Recognition Architecture
Most modern methods of ASR can be described as a combination of two models: the acoustic model, and the language model. They are combined simply by multiplying probabilities.
Jurafsky & Martin – Chapter 9 introduction
The difficulty of ASR depends on factors including vocabulary size, within- and across-speaker variability (including speaking style), and channel and environmental noise.
Holmes & Holmes – Chapter 8 – Template matching and dynamic time warping
Read up to the end of 8.5 carefully. Try to read 8.6 as part of Module 7, but rest assured we will go over the concept of dynamic programming again in Module 9. We recommend you should skim 8.7 and 8.8 because the same general concepts carry forward into Hidden Markov Models (again, we’ll come back to this in Module 9). You don’t need to read 8.9 onwards. Methods like DTW are rarely used now in state of the art systems, but are a good way to start understanding some core ideas.
Ladefoged (Elements) – Chapter 6 – Hearing
Some understanding of human hearing will be helpful for engineering suitable features to extract from the waveform for automatic speech recognition.
This is the new version. Still under construction.