Synthetic Speech Trained - Large Vocabulary Amharic Speech Recognition System (SST-LVASR)
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Date
2008-07
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Addis Ababa University
Abstract
Amharic is the official language of Ethiopia, which is characterized by very large
morphological forms of words. This thesis is an investigation of the possibility of
developing an Automatic speech recognition system (ASR) for Amharic using
synthesized Amharic speech generated through concatenation of prerecorded
morphemes, can be used to train a hidden markov model (HMM) based ASR system.
The development of HMM based ASR system requires identification of all possible
words and a construction of text and speech corpora containing multiple samples of
the words to be recognized by the system. These data are then used as training sets in
the development of the models, the final objective being the construction of HMM
models for each recognition unit. Since there are a large number of morphological
forms for the words in Amharic, the effort of collecting the Amharic words for
constructing the text corpus and the recording and labeling of the same words for the
speech corpus is extremely difficult. This thesis demonstrates that by developing an
automatic morphological expander, the effort of developing the text corpus is reduced
to a manageable level. Additionally, a significant reduction in the speech corpus
development is achieved by using machine generated speech for training the HMM
models of the ASR system. These reductions in the development efforts of the text
and speech corpora greatly reduce the most prominent of the obstacles in developing a
general purpose Amharic speech recognizer.
The 62.37% word accuracy for naturally recorded speech indicates that using
synthetic speech for training at least 62% of the words are correctly identified and
suggests that with synthetic speech some level of recognition is possible, giving the
imputes for more research in finding ways to increase this accuracy.
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Keywords
Recognition System, Amharic Speech