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DC Field | Value | Language |
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dc.contributor.author | Bimas Winahyu Aji | en |
dc.date.accessioned | 2010-06-16T03:56:23Z | en |
dc.date.available | 2010-06-16T03:56:23Z | en |
dc.date.copyright | 2008 | en |
dc.date.issued | 2008 | en |
dc.identifier.citation | Bimas Winahyu Aji. (2008). Speech recognition on the speechdat-car database. Master’s thesis, Nanyang Technological University, Singapore. | en |
dc.identifier.uri | https://hdl.handle.net/10356/40513 | en |
dc.description.abstract | The SpeechDat-Car database was built in the framework of the SpeechDat-Car project (European Union founded). The availability of the database allows the development of the speech recognition applications in mobile device under severe condition of car environment. A speaker-independent continuous Hidden Markov hlodel (CHMM) algorithm for speech recognition is to be produced in the project. To achieve this ob- jective, the CHMM system adaptations and testings on the SpeechDat-Car database would be investigated. This is done by first understanding the algorithm and evaluating both the front end and back end of the system, and then, modifying the algorithm for obtaining better performance. As the final algorithm adopted would be ported to a selected DSP, as given in the system specification, the work also focuses on algorithms with low computational cost and memory consumption. | en |
dc.format.extent | 135 p. | en |
dc.language.iso | en | en |
dc.subject | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition | en |
dc.title | Speech recognition on the speechdat-car database | en |
dc.type | Thesis | en |
dc.contributor.school | School of Computer Engineering | en |
dc.description.degree | MASTER OF ENGINEERING (SCE) | en |
dc.contributor.supervisor2 | He Yulan | en |
dc.identifier.doi | 10.32657/10356/40513 | en |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | SCSE Theses |
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File | Description | Size | Format | |
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BimasWinahyuAji08.pdf | 38.05 MB | Adobe PDF | View/Open |
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