Speech-based Question and Question Answering Classification for Afaan Oromo Language

Abstract

One of the information retrieval disciplines that accurately predicts answers to a given question from massive documents is question answering. Our research concentrated on developing an interactive model as a result. An interface using both Afaan Oromo speech recognition integrated with factoid question and answering. An automatic question classification system for speech-based questions for Afaan oromo question answering is what this project aims to design and build. After all, the study is integrate of both voice recognition and question-answering techniques. Numerous tools were used in the construction of the system's prototype. from those cygwin, python, perl and Neatbean 8.0 for Java coding. These study contains large number of Afaan Oromo documents for speech testing, training and also for answer extraction for question answering. The corpus collected from different Afaan Oromo newspaper online newspaper such as (Fana, Bariisaa, Bakkalcha and Ethiopres) and internet. We used 2,152 dataset for question-answering to evaluate the systems quality and also speech based question sentences corpus trains by 21 different people (male 13, and women 8 with total trains of 1344 speech dataset) those who can speak and read Afaan Oromo language and tested by both who trains and not trained. Each individual reads 64 questions aloud, and the questions types are about places and person. The model provided recognition accuracy of 80.2% with 19.8% WER. The speech recognition system's experimental findings showed accuracy of 78.4%. The question classification without question and answering for both person and place question types classified with a 98% and 96% for both questions list respectively. But with question and answering the Rule based question classification accurate 89.1% precision, 91.6% recall and 90.3% F-measurement. The results of speechbased questions and automatic question classification for Afaan Oromo questionanswering are generally achieves 71.45% accuracy. The challenges with this research is that it did not parse a query using synonyms. As a result, in order to improve the performance of Speech-based question and Afaan Oromo question answering Classification system, semantic similarity using ontology-based structure is needed.