dc.contributor.author |
Badenhorst, J
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|
dc.contributor.author |
Davel, MH
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|
dc.date.accessioned |
2016-08-22T11:36:42Z |
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dc.date.available |
2016-08-22T11:36:42Z |
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dc.date.issued |
2015-11 |
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dc.identifier.citation |
Badenhorst, J and Davel, MH. 2015. Synthetic triphones from trajectory-based feature distributions. In: Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobTech), Port Elizabeth, South Africa, 25-26 November 2015 |
en_US |
dc.identifier.uri |
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7359509&tag=1
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dc.identifier.uri |
http://hdl.handle.net/10204/8737
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dc.description |
Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobTech), Port Elizabeth, South Africa, 25-26 November 2015 |
en_US |
dc.description.abstract |
We experiment with a new method to create synthetic models of rare and unseen triphones in order to supplement limited automatic speech recognition (ASR) training data. A trajectory model is used to characterise seen transitions at the spectral level, and these models are then used to create features for unseen or rare triphones. We find that a fairly restricted model (piece-wise linear with three line segments per channel of a diphone transition) is able to represent training data quite accurately. We report on initial results when creating additional triphones for a single-speaker data set, finding small but significant gains, especially when adding additional samples of rare (rather than unseen) triphones. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Workflow;16011 |
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dc.subject |
Synthetic triphones |
en_US |
dc.subject |
Trajectory modelling |
en_US |
dc.subject |
Trajectory-based features |
en_US |
dc.subject |
Feature distributions |
en_US |
dc.subject |
Feature construction |
en_US |
dc.title |
Synthetic triphones from trajectory-based feature distributions |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Badenhorst, J., & Davel, M. (2015). Synthetic triphones from trajectory-based feature distributions. IEEE. http://hdl.handle.net/10204/8737 |
en_ZA |
dc.identifier.chicagocitation |
Badenhorst, J, and MH Davel. "Synthetic triphones from trajectory-based feature distributions." (2015): http://hdl.handle.net/10204/8737 |
en_ZA |
dc.identifier.vancouvercitation |
Badenhorst J, Davel M, Synthetic triphones from trajectory-based feature distributions; IEEE; 2015. http://hdl.handle.net/10204/8737 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Badenhorst, J
AU - Davel, MH
AB - We experiment with a new method to create synthetic models of rare and unseen triphones in order to supplement limited automatic speech recognition (ASR) training data. A trajectory model is used to characterise seen transitions at the spectral level, and these models are then used to create features for unseen or rare triphones. We find that a fairly restricted model (piece-wise linear with three line segments per channel of a diphone transition) is able to represent training data quite accurately. We report on initial results when creating additional triphones for a single-speaker data set, finding small but significant gains, especially when adding additional samples of rare (rather than unseen) triphones.
DA - 2015-11
DB - ResearchSpace
DP - CSIR
KW - Synthetic triphones
KW - Trajectory modelling
KW - Trajectory-based features
KW - Feature distributions
KW - Feature construction
LK - https://researchspace.csir.co.za
PY - 2015
T1 - Synthetic triphones from trajectory-based feature distributions
TI - Synthetic triphones from trajectory-based feature distributions
UR - http://hdl.handle.net/10204/8737
ER -
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en_ZA |