Jurafsky & Martin – Section 9.3 – Feature Extraction: MFCCs

Mel-frequency Cepstral Co-efficients are a widely-used feature with HMM acoustic models. They are a classic example of feature engineering: manipulating the extracted features to suit the properties and limitations of the statistical model.

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.