Biru, TesfayeBirhanu, SolomonTaddesse, KnfeSeifu, Zegaye2018-11-302023-11-182018-11-302023-11-182003-06http://etd.aau.edu.et/handle/12345678/14770This study investigated the possibility of developing large vocabulary, speaker independent and continuous speech recognizer for Amharic language. The recognizer was developed using Hidden Markov Model; and the Hidden Markov Modeling Toolkit was used to implement it. In the process, a corpus developed by Solomon Tefera was used to get the required data for training and testing the models. It was a database comprised of 8000 utterances that were used for training and 500 plus sentences for development and evaluation. The data was preprocessed in line with the requirements of the HTK toolkit. In order to support the acoustic models, a bigram language model was constructed. In addition, pronunciation dictionary was prepared and used as an input. Since the recognizer was meant to recognize large vocabulary and continuous speech, phonemes were opted as the basic unit of recognition. However phonemes are known to be context independent units, given that the environment in which a sound is put makes a difference in the way it is pronounced. Thus after the monophone based speech recognizer was built, it was promoted to triphone based system in which the left and right contexts were considered and modeled. Besides, the mixture components of the states of the models were incremented in view of optimizing the performance of the recognizers.enSpeech RecognitionThesis