Speaker Dependent Speech Recognition For Wolaytta Language

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Addis Ababa University


Speech recognition systems are built and researches are conducted world wide to meet the needs of different languages. In Ethiopia, speech recognition researches were carried out for languages like Amharic, Oromifa, and Tigrigna. These works have paved the way for researches on other languages like Wolaytta. A speaker dependent approach to speech recognition was proposed and adopted for the Wolaytta language in this research. Word level context independent and word level context dependent models and recognizers were built based on an HMM based open source speech recognition tool known as Sphinx. A speech corpus was constructed from utterances made by using 450 distinct words which were selected upon domain expert consultation, and were each recorded in a single audio file to make a total of 450 audio files. Two independent tests were carried out on this corpus. The first test involved randomly selecting 100 words from the training corpus (which consisted of 300 words) and testing the model using these set of words. The second test involved using 150 words that were not used in training the model and testing the model with these set of words. After the data preparation, preprocessing activities mandatory for model building were carried out such as preparing dictionary and phone set for Wolaytta, and extracting the feature vectors of the audio files used in training. Tests were carried out on the test data for both models, and an accuracy of 53% was achieved for the context-dependent model, while accuracy of 41% was retrieved for the context-independent model. This research work can further be extended and the results can be enhanced as per the recommendations made by the researcher. Key terms: Context-dependent, Context-independent, HMM, Speaker dependent, Speech Recognition, Sphinx, Wolaytta



Context-dependent, Context-independent, HMM, Speaker dependent, Speech Recognition, Sphinx, Wolaytta