I am implementing a speech recognition system based on Hidden Markov models. I
have finished the Baum-Welch algorithm for model training - and tested it on
some primitive models.
I work with discrete HMM, so I am using a code book with 50 output symbols. For
speech parametrization I am using 12 MFCC coefficients for every block of
sound(512 sound samples - 30 ms).
Now when I am training the model on several speech samples (10 times the same
word is recorded). I am getting the emission probability of some of the symbols
from code book equal ZERO in all model states (after training). Thant wouldn't
be a problem. But sometimes during recognition process I get a symbol, which has
for this particular model probability of emission in all states equal 0. (Even
when it is the same word). That's why, the total probabilty for this word to be
generated by this model is equal ZERO too. (and that is obviously wrong)
Is there any posibility to avoid this situation? (Maybe more sound samples
during model training - to cover all the posibilities how the word can be spoken
by the speaker). I haven't seen any note about this problem in articles about
HMM and speech recognition.
I hope I explained my situation clear enough.
Thanks in advance.
Hidden Markov model - zero probability problem
Started by ●December 7, 2010