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DC Field | Value | Language |
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dc.contributor.author | Kimutai, Shadrack K. | - |
dc.contributor.author | Milgo, Edna | - |
dc.contributor.author | Gichoya, David | - |
dc.date.accessioned | 2022-03-07T08:59:35Z | - |
dc.date.available | 2022-03-07T08:59:35Z | - |
dc.date.issued | 2013 | - |
dc.identifier.uri | http://ir.mu.ac.ke:8080/jspui/handle/123456789/6060 | - |
dc.description.abstract | Speech recognition is one of the frontiers in Human Computer Interaction. A number of tools used to achieve speech recognition are currently available. One of such tools is Sphinx4 from Carnegie Mellon University (CMU). It has a recognition engine based on discrete Hidden Markov Model (dHMM) and a modular structure making it flexible to a diverse set of requirements. However, most efforts that have been undertaken using this tool are focused on established dialects such as English and French. Despite Swahili being a major spoken language in Africa, literature search indicates that little research has been undertaken in developing a speech recognition tool for this dialect. In this paper, we propose an approach to building a Swahili speech recognizer using Sphinx4 to demonstrate its adaptability to recognition of spoken Swahili words. To realize this, we examined the Swahili language structure and sound synthesis processes. Then, a 40 word Swahili acoustic model was built based on the observed language and sound structures using CMU Sphinx train and associate tools. The developed acoustic model was then tested using sphinx4. | en_US |
dc.language.iso | en | en_US |
dc.publisher | International Journal of Emerging Science and Engineering | en_US |
dc.subject | Sphinx4 | en_US |
dc.subject | Swahili language | en_US |
dc.subject | Speech recognition | en_US |
dc.subject | Hidden Markov model. | en_US |
dc.title | Isolated Swahili words recognition using Sphinx4 | en_US |
dc.type | Article | en_US |
Appears in Collections: | School of Information Sciences |
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Edna M. etal.pdf | 456.54 kB | Adobe PDF | View/Open |
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