A General Approach for Amharic SPEECH-TO-TEXT Recognition
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Date
2009-10
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Addis Ababa University
Abstract
In this paper, the researcher has tried to investigate the capability of exploring speech
recognition technique for converting an Amharic speech to text taking native and non
native speaker of the language Amharic. For this, Hidden Markov Model (HMM) is used
as a model along with the tool Hidden Markov Modeling Toolkit (HTK) to implement
and get the desired result out of the training.
In the development process, a total of four hundred sentences are used for the training
and hundred data sets which are not included in the training set are used for testing the
performance of the speech recognizer.
The primary data has been collected from four different ethnic groups that could not
speak out Amharic as a mother tongue and one from the mother tongue, with an input of
hundred records from each group and secondary data from the previous researcher. Then,
the primary data has been labeled, preprocessed, trained and realigned as per the
requirement of the HTK for the purpose of training and testing the models.
During the experiment process, a lot of challenging issue pointed out which makes the
researcher to draw attention in order to confront the problems that suspend the success
from attainment to the point of end.
As a final point, through all this complication, the existence of the research comes to the
end provided the constraint the result obtained is promising and serve as a proof that it is
possible to build general speech recognition technique that convert an Amharic SPEECHTO-
TEXT using HMM.
Once the experiment is completed and result obtained, analysis on the result is forwarded
through the justification that are supported by different researcher in addition to the
comparison of the result obtained.
As a final point, conclusion and recommendation are forwarded for the upcoming
research area in the field.
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Keywords
Amharic SPEECH-TO-TEXT