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From: jorge on 3 May 2010 06:21
Hi, I'm working on a speech recognition project. I'm trying to use Hidden Markov Models as a classifier.
I have few question about it.
For example I have a training set of speech files(10 different utterances, each utterance is pronounced 10 times). For each file Im doing feature extraction: every file is divided on 5 frames (constant number of samples - length(frame)==const) and for each frame I've got 12 MFCC coefficients.
How to build a HMM to recognize 10 diffrent words having training data?
I think, I must have 10 different HM models (for each word).
What about structure of models?
I think, every model must have 5 states (becouse every file is dividing to 5 frames).
In each state I have 12 MFCC coefficients as an obsevations.
In theory I know, how recognition proces will be proceded. But I dont know how do this in MATLAB.
Do I need vector quantization for my features? (How can I do it?)
How to build HMM models structure? (every state should have a loop on themselves, and transition to next state, without returning to previous state - am I right?)
If you have any advice I will be greatfull.
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