Reading
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.
Holmes & Holmes – Chapter 10 – Front-end analysis for ASR
Covers filterbank, MFCC features. The material on linear prediction is out of scope.
Taylor – Section 12.3 – The cepstrum
By using the logarithm to convert a multiplication into a sum, the cepstrum separates the source and filter components of speech.
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.