> Sir i have problem using HMM for speech recognition....
> My problem statement is that i want to identify the sequence of arabic
> phonemes present in the speech file... I have calculated certain
> features like MFCC, wavelet, spectrogram etc for the speech file. These
> features are in numerical format ( not probabilities ). There are 28
> arabic phonemes. I have studied some literature related to HMM, which
> require some data as emission matrix, transition matrix, states,
> observations/emissions/outcomes....
OK, so the number of states is 28 (or 29 if you want to include
silences).
> sir how can i model my problem using HMM. How can i calculate
> transition matrix ??? as phoneme can occur in either order in speech
> file. There is no particular rule which can tell me about the
> probability of a phoneme to be the next one. Similarly how can i use
> feature vector in emission matrix as emission marix requires that every
> row's probability sum should be 1.
Google on "Baum-Welch".
Any HMM is usually described by the triple (A, B, pi).
A contains your state transition probabilities (29 x 29).
B contains the probability that state n generates observation m; If
your phonemes are ordered so that like ones are adjacent states, then
you could model B as a 29 x 29 block-diagonal matrix (the off-diagonal
probabilities indicating the probability that an adjacent phoneme is
mis-recognised).
pi is the initial state probability (set this to 1 in the "silence"
state and zero for all 28 phoneme states).
Then the Baum-Welch algorithm allows you to "learn" (A, B, pi) given
sufficient data.
> which item is representing my states. ( feature vector or phoneme set )
Your phonemes are the unknowns (and unmeasureables). These are your
states.
> which item is representing my observations/emissions. ( feature vector
> or phoneme set )
The things you can measure are your feature set, so these are
"observable" and are your observations.
> If u have some idea to use HMM for my problem, kindly send me your
> suggestion in this regard....
I hope this isn't a homework problem. :-)
Ciao,
Peter K.
Reply by ahmad●June 9, 20052005-06-09
Sir i have problem using HMM for speech recognition....
My problem statement is that i want to identify the sequence of arabic
phonemes present in the speech file... I have calculated certain
features like MFCC, wavelet, spectrogram etc for the speech file. These
features are in numerical format ( not probabilities ). There are 28
arabic phonemes. I have studied some literature related to HMM, which
require some data as emission matrix, transition matrix, states,
observations/emissions/outcomes....
sir how can i model my problem using HMM. How can i calculate
transition matrix ??? as phoneme can occur in either order in speech
file. There is no particular rule which can tell me about the
probability of a phoneme to be the next one. Similarly how can i use
feature vector in emission matrix as emission marix requires that every
row's probability sum should be 1.
which item is representing my states. ( feature vector or phoneme set )
which item is representing my observations/emissions. ( feature vector
or phoneme set )
If u have some idea to use HMM for my problem, kindly send me your
suggestion in this regard....