###### Abstract  
Speech 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. Keywords:database; deep learning; consonant/vowel/silence; segmentation; speech segment detection +
Footnote †: journal: Medical  
vocal. This makes it difficult to distinguish between vowel phonemes and consonant phonemes [2]. It is possible to categorize words into various types in accordance with the position of the occurrences of vowels and consonants in them. The placement of vowels and consonants in a word of any language is very significant for identifying various types of words [3]. The acoustic phoneme constitutes the basis of a wide variety of the existing speech processing systems, such as medium to large vocabulary speech recognition, speaker recognition systems, and language identification systems [4]. The accuracy of a speech processing system highly depends on the spoken language phoneme set and has more challenges due to alterations in the pronunciation of words due to dialects, the speaker's age and gender, and neighboring words. Furthermore, certain difficulties come to the forefront during the creation of a voice database. In addition, the voice segment detection process becomes more difficult due to factors including the toning effect and syllable stress. In this respect, the gated recurrent unit (GRU), which is a special recurrent neural network (RNN) model, was used to deal with these difficulties for the efficient speech segment detection. This model is now widely used in various speech processing tasks. In [PERSON] et al. [5], GRUs were reviewed and a compact single-gate model replacing a hyperbolic tangent with rectified linear unit activations having a simplified architecture was suggested for the purpose of automatic speech recognition (ASR). The long short-term memory (LSTM) and GRU were assessed for the purpose of comparing the performances they exhibited upon a reduced Technology Entertainment Design - Laboratoire Informatique de l'Universite du Maine (TED-LIUM) speech data set [6]. In [PERSON] and [PERSON] [7], a solution was suggested with the aim of training a phone attribute detector without phone alignment by utilizing end-to-end phone attribute modeling on the basis of the connectionist temporal classification. In [PERSON] et al. [8], general emotion features were produced in speech signals from various angles, and an ensemble learning model was employed for the purpose of carrying out emotion recognition tasks. The design of the expert roles of speech emotion recognition was performed by utilizing convolutional neural networks (CNNs) and GRU. In [PERSON] et al. [9], a practical approach with three steps was introduced for singing voice detection on the basis of a GRU. Recently, speech segment detection has been addressed in a deep neural network (DNN), CNN, and RNN models [10; 11; 12].