# Classification of observation symbols in a HMM?

I'm new to concept of HMMs. I have trained 2 HMMs separately.

HMM1 is trained with symbols A, B, C.

HMM2 is trained with symbols D, E, F.

I have a set of observation symbols in the set V={A,B,C,D,E,F}.

In testing phase, I'm extracting a symbol by trying to associate a test vector to one of the symbols in V (using euclidean distance to cluster centers).

How would I determine the log likelihood of a Observation Sequence A, B, C,... if that HMM was only trained with the symbols D, E, F....?

Or is it that log likelihood isn't defined in this case?

Is it necessary that observation symbols have to be shared among various HMMs? i.e picked from a common observation Symbol set V?

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have you tried looking at iohmm (input-output hmm) ? might be more suitable for this case. – Ran Feb 15 '12 at 7:24