We have derived an expression for the integral of the DAF-HMM
likelihood function with respect to the un-augmented features.
This allows normalizing the DAF-HMM likelihood function so that it can be
compared with likelihood functions based on the un-augmented features.
We demonstrated the use of the method on two data sets.
In particular, we have shown that appending feature time derivatives
achieves lower classification error as well as
higher average log-likelihood for
data with slowly-varying spectral character.
For data with abrupt spectral character,
the opposite was observed. This indicates
the possibility of a ``litmus test" for
the use of DAF. It suggests the possibility of using
DAF-HMM along with HMM togeter in a single classifier.