International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 11, No. 5, 8 May 2019
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Optimizing Parameters of Automatic Speech Segmentation into Syllable Units
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Author(s)
Riksa Meidy Karim
1,*
Suyanto
1
Index Terms
Boundary detection, genetic algorithm, iterative-splitting, iterative-assimilation, parameter optimization, syllable segmentation
Abstract
An automatic speech segmentation into syllable is an important task in a modern syllable-based speech recognition. It is generally developed using a time-domain energy-based feature and a static threshold to detect a syllable boundary. The main problem is the fixed threshold should be defined exhaustively to get a high generalized accuracy. In this paper, an optimization method is proposed to adaptively find the best threshold. It optimizes the parameters of syllable speech segmentation and exploits two post-processing methods: iterative-splitting and iterative-assimilation. The optimization is carried out using three independent genetic algorithms (GAs) for three processes: boundary detection, iterative-splitting, and iterative-assimilation. Testing to an Indonesian speech dataset of 110 utterances shows that the proposed iterative-splitting with optimum parameters reduce deletion errors more than the commonly used non-iterative-splitting. The optimized iterative-assimilation is capable of removing more insertions, without over-merging, than the common non-iterative-assimilation. The sequential combination of optimized iterative-splitting and optimized iterative-assimilation gives the highest accuracy with the lowest deletion and insertion errors.
Cite This Paper
Riksa Meidy Karim, Suyanto, "Optimizing Parameters of Automatic Speech Segmentation into Syllable Units", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.5, pp.9-17 2019. DOI:10.5815/ijisa.2019.05.02
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