Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97868
Title: Model-based articulatory phonetic features for improved speech recognition
Authors: Huang, Guangpu
Er, Meng Joo
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Huang, G., & Er, M. J. (2012). Model-based articulatory phonetic features for improved speech recognition. The 2012 International Joint Conference on Neural Networks (IJCNN).
Abstract: We describe a neural based articulatory phonetic inversion model to improve the recognition of the acoustically varying vowels and the syllable initial plosives. The model uses a set of continuous valued articulatory phonetic features (APFs) to explore the interactions between the motor control of articulators and the acoustic phonetic events. We demonstrate that the neural model gives more accurate and robust recognition performance on the TIMIT sentences. The model offers two salient properties: it allows asynchronous feature changes at phoneme boundaries, and it accounts for the dual aspects of human speech production and perception through a heuristic learning algorithm during APFs mapping.
URI: https://hdl.handle.net/10356/97868
http://hdl.handle.net/10220/12393
DOI: 10.1109/IJCNN.2012.6252748
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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