A Continuous, Speaker Independent Speech Recognizer for Afaan Oromoo

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Date

2010-07

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Addis Ababa University

Abstract

The ultimate goal of any automatic speech recognition is towards developing a model that converts speech utterance to texts words. Therefore, a continuous, speaker independent Afaan Oromoo speech recogniser’s experiment is performed having similar objective of transforming Afaan Oromoo continuous speech in to its text word formats for continuous Afaan Oromoo speaker independent speech utterances using HMM and sphinx system (sphinx train for training and Sphinx4 for decoding). Therefore, this research tries to develop prototype for a continuous, speaker independent Afaan Oromoo speech recognizer so as to check possibility and suitability of the tools and techniques selected from the various literatures. A continuous, speaker independent Afaan Oromoo speech recognizer is developed in this research work for 70 selected Afaan Oromoo long words, phrase and simple sentence uttered by 30 selected peoples from different age group and sex constituting of 2100 utterances. Accordingly, the data collected was divided in the 2/3 by 1/3 for training and testing respectively. Furthermore, various preprocessing and other activities were performed including building the acoustic and language models among others which might greatly affect significantly the performance of the recognizer. These Afaan Oromoo selected words, phrases and simple sentences are selected in consultation of the domain experts. For this research performance evaluation is performed using test data sets and the recognizer performance is found to be 68.514% with sentence accuracy of 28% for continuous Afaan Oromoo speech and a phoneme based trigram performance of 89.459% with sentence accuracy of 42% achieved. According to the finding of this research, the performance gained for Afaan Oromoo language is highly promising and as the language is becoming one of the most spoken language of the country, it will have tantamount for latter full deployment of the recognizer in the language. Keywords: Speech recognition, Afaan Oromoo, sphinx, Hidden Markov Model

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Keywords

Speech recognition, Afaan Oromoo, sphinx, Hidden Markov Model

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