Sub-Word Based Amharic Word Recognition: an Experiment Using Hidden Markov Model (Hmm)

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Addis Ababa University


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 recognizer the popular toolkit Hidden Markov Model Toolkit (HTK) was used.In the process of building the recognizer, 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 technique whereby one HMM is constructed for each sub-word unit (phoneme, trip hone, syllable, etc .). Phonemes, tied-state trip hones and CV-syllables have been considered as the basic sub-word units and have been used to build phoneme-based, tied-state trip hone based and CV -syllable based recognizer, respectively. In this study, an extensible l70-word vocabulary is constructed and both speaker-dependent and speaker-independent models are built using 15 speakers (8 male and 7 female) in the age range of 20 to 30. 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 trip hones 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 underscored for Fruther research.



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