The combination of evidences from the three different aspects of speech provides better discrimination among different sonorant classes, compared to the baseline MFCC features. Correlation of speech over ten consecutive pitch periods is used as the suprasegmental feature representing periodicity information. A feature representing strength of excitation is derived from the Hilbert envelope of linear prediction residual, which represents the source information. A five-dimensional feature set is computed from the estimated formants to measure the prominence of the spectral peaks. It is derived from zero time windowed speech signal that provides better resolution of the formants. Vocal-tract system information is extracted from the Hilbert envelope of numerator of group delay function. In this work, the vocal-tract system, excitation source and suprasegmental features derived from the speech signal are analyzed to measure the sonority information present in each of them. Sonorant sounds are characterized by regions with prominent formant structure, high energy and high degree of periodicity. Transliteration, word boundary prediction, or OpticalĬharacter Recognition for Javanese scripts. Of Javanese syllables for more complex applications such as ![]() Signifies that our syllabifier is capable of providing a corpus Words into syllables achieves 95.56% for data set scrappedįrom Wiki and 97.92% for data set taken from Javanese The experiment shows that the accuracy rate of segmented Rules are based on the orthograpic system of Javanese script. Syllabary scripts, word recognition, and speech synthesis.ĭue to the lack of data set and resources, this researchĪpplied a Finite State Transducer model to build a syllabifierįor Javanese documents written in Latin. In any task related to transliteration process for Abugida or Syllabification becomes the basic backbone Still badly needed in under-resourced and critical languages ![]() Automatic syllabification is considered as aįinished process in high-resource languages.
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