How to decide the number of cepstral coefficients?
From CMUSphinx FAQ:


  There are various types of MFCC which differ by number of parameters,
  but not really different for accuracy (it might be a few percent worse
  or better).
  
  The interpretation of MFCC (Roughtly introduced Alan V. Oppenheim and
  Ronald W. Schafer. From Frequency to Quefrency: A History of the
  Cepstrum. IEEE SIGNAL PROCESSING MAGAZINE) is not applicable as such,
  and the use of 12 or 13 coefficients seem to be due to historical
  reasons in many of the reported cases. The choice of the number of
  MFCCs to include in an ASR system is largely empirical. To understand
  why any specific number of cepstral coefficients is used, you could do
  worse than look at very early (pre-HMM) papers. When using DTW using
  Euclidean or even Mahalanobis distances, it quickly became apparent
  that the very high cepstral coefficients were not helpful for
  recognition, and to a lesser extent, neither were the very low ones.
  The most common solution was to “lifter” the MFCCs - i.e. apply a
  weighting function to them to emphasise the mid-range coefficients.
  These liftering functions were “optimised” by a number of researchers,
  but they almost always ended up being close to zero by the time you
  got to the 12th coefficient.
  
  In practice, the optimal number of coefficients depends on the
  quantity of training data, the details of the training algorithm (in
  particular how well the PDFs can be modelled as the dimensionality of
  the feature space increases), the number of Gaussian mixtures in the
  HMMs, the speaker and background noise characteristics, and sometimes
  the available computing resources.