- section 2.2.1: basic definition of a finite-state automata (used for dialogue management)
- section 3.11: minimum edit distance (used for e.g. speech recognition evaluation)
- section 4.2: N-gram models (used for language modelling in speech recognition)
- sections 6.1 and 6.2: basic probabilistic modelling with Markov Chains and Hidden Markov Models
- section 7.1: phonetic transcription
- section 7.2 and 7.3: basics of articulatory phonetics and pronunciation variation (the general principles, not all the details)
- section 7.4: basics of acoustic phonetics (idem)
- sections 8.1, 8.2, 8.3 and 8.4: read once, but without going into all the details
- section 8.5: understand how unit selection works and how the constraints are realised
- section 9.1 and 9.2: important concepts to understand!
- section 9.3 and 9.4: basic principles, but not the details
- section 9.8: how WER is calculated
- section 12.8: how spoken language syntax differs from written language
- section 17.6: embodiment and situation (links to our discussion of human-robot interaction and situated language processing)
- sections 21.3, 21.4 and 21.6.3: how reference resolution works
- Finally, the whole Chapter 24 should be read