Newsgroups: comp.speech
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From: jjesus@casip.ugr.es (Jose Jesus Fernandez Rodriguez)
Subject: Problem with HMM training.
Message-ID: <1993Sep27.170912.9710@sevaxu.cica.es>
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Nntp-Posting-Host: babieca.ugr.es
Reply-To: jjesus@casip.ugr.es
Organization: Dept.Electronica (Univ.Granada)
Date: Mon, 27 Sep 1993 17:09:12 GMT
Lines: 37

Hi everyone!!.

I have got a question to ask to all of you: Masters of ASR!! 

We have developed an hybrid system for Automatic Speech Recognition, 
specifically, for speaker independent isolated word recognition.
Our system consist of a Multilayer Perceptron (MLP), which acts as phonematic
recognizer, and a set of HMMs, which function is to process the sequences
of phonemes generated by the MLP.

In order to train the set of HMMs, we have employ the ML algorithm (Maximum
Likelihood, or Baum-Welch algorithm). With this configuration, we have obtained
a max. test recognition rate of 94.25 %. And training sequences were
recognized in the range 99.7 to 99.8 %. Also, we try a system with a 
different number of states per HMM, and we were succeded in improving test rate 
to 94.5 %.

When we got these rates, in order to improve them, we decided to employ a 
discriminative algorithm to train the HMMs: And we used the MMI algorithm
(Bahl, Brown, de Souza, Mercer: "Maximum mutual information estimation of
hidden Markov model parameters for speech recognition". Proc. ICASSP, 1986).
As a result, we have achieved a max. training seq. rate of even 100%,
however, the max. test recog. rate does not exceed 92.25%. We have tried to make
some modifications to this algorithm, but we have not obtained any improvement.

I would like to ask you,... 

why don't the system of HMMs trained with MMI algorithm generalize?, i.e.
why does it improve train. rate whereas make test rate worse?

I would be grateful to you for any comments.

--
Jose Jesus - jjesus@casip.ugr.es   
Departament of Electronics & Computer Technology.
University of Granada. Spain. 

