Browsing by Author "Tadesse, Kinfe"

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    Sub-Word Based Amharic Word Wecognition : an Experiment using Hidden Markov Model (HMM)
    (Addis Ababa University, 2002-06) Tadesse, Kinfe; Berhanu, Solomon
    In this study, the potential of Hidden Markov Model (HMM) for the development of Amharic speech recognition system has been investigated and in the course of building the recognizers the popular toolkit Hidden Markov Model Toolkit (HTK) was used. In the process of building the recognizers, the speech data is recorded at a sampling rate of 16KHz and the recorded speech is then converted into Mel Frequency Cepstral Coefficient (MFCC) vectors for further analysis and processing. Since large vocabulary systems are envisaged, sub-word modeling is pursued. Sub-word modeling refers to a technique whereby one HMM is constructed for each sub-word unit (phoneme, triphone, syllable, etc.). Phonemes, tied-state triphones and CV-syllables have been considered as the basic sub-word units and are used to build phoneme-based, tiedstate triphone based and CV-syllable based recognizers respectively. In this study, an extensible 170 word vocabulary is constructed and both speakerdependent and speaker-independent models are built for 15 speakers (8 male and 7 female) in the age range of 20 to 30 using phonemes and tied-state triphones as the basic units of recognition. Five untrained speakers who had no involvement in training the models are also used to test the speaker-independent models. The results obtained are promising and have shown the potential of tied-state triphones as good sub-word units for Amharic. In fact, phonemes also have produced encouraging recognition performance. Even though CV-syllables appear to be more convenient for Amharic, this research has not proved that and is recommended for further research.