An Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection

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dc.authoridAYDIN, Nizamettin/0000-0003-0022-2247
dc.contributor.authorBatur Dinler, Ozlem
dc.contributor.authorAydin, Nizamettin
dc.date.accessioned2024-12-24T19:33:31Z
dc.date.available2024-12-24T19:33:31Z
dc.date.issued2020
dc.departmentSiirt Üniversitesi
dc.description.abstractSpeech segment detection based on gated recurrent unit (GRU) recurrent neural networks for the Kurdish language was investigated in the present study. The novelties of the current research are the utilization of a GRU in Kurdish speech segment detection, creation of a unique database from the Kurdish language, and optimization of processing parameters for Kurdish speech segmentation. This study is the first attempt to find the optimal feature parameters of the model and to form a large Kurdish vocabulary dataset for a speech segment detection based on consonant, vowel, and silence (C/V/S) discrimination. For this purpose, four window sizes and three window types with three hybrid feature vector techniques were used to describe the phoneme boundaries. Identification of the phoneme boundaries using a GRU recurrent neural network was performed with six different classification algorithms for the C/V/S discrimination. We have demonstrated that the GRU model has achieved outstanding speech segmentation performance for characterizing Kurdish acoustic signals. The experimental findings of the present study show the significance of the segment detection of speech signals by effectively utilizing hybrid features, window sizes, window types, and classification models for Kurdish speech.
dc.identifier.doi10.3390/app10041273
dc.identifier.issn2076-3417
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85081197881
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app10041273
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8180
dc.identifier.volume10
dc.identifier.wosWOS:000525287900080
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectdatabase
dc.subjectdeep learning
dc.subjectconsonant
dc.subjectvowel
dc.subjectsilence
dc.subjectsegmentation
dc.subjectspeech segment detection
dc.titleAn Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection
dc.typeArticle

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