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 – 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 9.2 – The HMM Applied to Speech
Introduces some notation and the basic concepts of HMMs.
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.
Taylor – Chapter 5 – Text decoding
Complementary to Jurafsky & Martin, Section 8.1.
Taylor – Chapter 6 – Prosody prediction from text
Predicting phrasing, prominence, intonation and tune, from text input.
Jurafsky & Martin – Section 8.4 – Diphone Waveform Synthesis
A simple way to generate a waveform is by concatenating speech units from a pre-recorded database. The database contains one recording of each required speech unit.
Taylor – Section 10.2 – Digital signals
Going digital involves approximations in the way an original analogue signal is represented.
Taylor – Section 10.1 – Analogue signals
It’s easier to start by understanding physical signals – which are analogue – before we then approximate them digitally.
Jurafsky & Martin (2nd ed) – Section 8.3 – Prosodic Analysis
Beyond getting the phones right, we also need to consider other aspects of speech such as intonation and pausing.
This is the new version. Still under construction.