Thesis (M.Sc. (Computer Science)) -- University of Limpopo, 2015 / This study investigates the potential of using graphemes, instead of phonemes, as acoustic sub-word units for monolingual and cross-lingual speech recognition for some of the under-resourced languages of the Limpopo Province, namely, IsiNdebele, Sepedi and Tshivenda. The performance of a grapheme-based recognition system is compared to that of phoneme-based recognition system.
For each selected under-resourced language, automatic speech recognition (ASR) system based on the use of hidden Markov models (HMMs) was developed using both graphemes and phonemes as acoustic sub-word units. The ASR framework used models emission distributions by 16 Gaussian Mixture Models (GMMs) with 2 mixture increments. A third-order n-gram language model was used in all experiments. Identical speech datasets were used for each experiment per language. The LWAZI speech corpora and the National Centre for Human Language Technologies (NCHLT) speech corpora were used for training and testing the tied-state context-dependent acoustic models. The performance of all systems was evaluated at the word-level recognition using word error rate (WER).
The results of our study show that grapheme-based continuous speech recognition, which copes with the problem of low-quality or unavailable pronunciation dictionaries, is comparable to phoneme-based recognition for the selected under-resourced languages in both the monolingual and cross-lingual speech recognition tasks. The study significantly demonstrates that context-dependent grapheme-based sub-word units can be reliable for small and medium-large vocabulary speech recognition tasks for these languages. / Telkom SA
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ul/oai:ulspace.ul.ac.za:10386/1615 |
| Date | January 2015 |
| Creators | Manaileng, Mabu Johannes |
| Contributors | Manamela, M.J.D, Velempini, M. |
| Publisher | University of Limpopo |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | xv, 105 leaves |
| Relation |
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