Optimizing parameters of automatic speech segmentation into syllable units
Автор: Riksa Meidy Karim, Suyanto
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 5 vol.11, 2019 года.
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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.
Boundary detection, genetic algorithm, iterative-splitting, iterative-assimilation, parameter optimization, syllable segmentation
Короткий адрес: https://sciup.org/15016591
IDR: 15016591 | DOI: 10.5815/ijisa.2019.05.02
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