###### Abstract
[PERSON] et al. [11] examined the automatic detection of phoneme boundaries in audio recordings using deep bidirectional LSTMs. The first experiments operated on Texas Instruments Massachusetts Institute of Technology (TIMIT) and BUCKEYE datasets containing an American English language speech corpus and then a Basaa dataset containing a Bantu language speech corpus. An F1-score of 77 with a tolerance of 20 ms was achieved with the above-mentioned method. In [PERSON] et al. [12], it is reported that GRU forget gate activations in trained recurrent acoustic neural networks correlate very well with phoneme boundaries in speech activation, which ensures the validation of numerous approaches to speech recognition and other sequence modeling problems, including recurrent networks. This study uses a TIMIT corpus. The GRU can take temporal context into account. Therefore, it should exhibit a better performance in comparison with conventional machine learning techniques [13]. Alternatively, LSTM is a slightly more complex structure variation of the GRU [14]. The GRU ensures the control of the information flow, similar to the LSTM unit, but without a need to utilize a memory unit. It only provides the exposure of the complete hidden content without any control. The GRU has a simpler structure compared to standard LSTM models, and its popularity is gradually increasing. Therefore, it is preferable for this study. [PERSON] et al. [15] suggested phoneme segmentation by utilizing cross-entropy loss with connectionist temporal classification loss in deep speech architecture for the purpose of performing speech synthesis. In order to assess the suggested method, female Korean speech found in the Speech Information Technology & Industry Promotion Center (SITEC) speech database was utilized. The suggested method ensured the improvement in the quality of phoneme segmentation since it could provide the model with a higher number of non-blank classes or force the setting of a blank class to a non-blank class. The experimental findings demonstrated that a decrease of more than 20% occurred in the boundary error. In [PERSON] and [PERSON] [16], a comparison of a bidirectional LSTM with other neural network architectures for the purpose of framewise phoneme classification on the TIMIT speech database was made. In the past, speech segment detection has been addressed in different conventional models. In [PERSON] et al. [17], the development of a system to conduct the acoustic-phonetic analysis of continuous speech was performed with the aim of serving as a part of an automatic speech understanding system. In [PERSON] et al. [18], a segmental approach was employed for the purpose of phonetic recognition. Multi-layer perceptrons were utilized to detect and classify phonemes for a segmental framework. In [PERSON] et al. [19], the acoustic and phonetic properties of the American English stop consonants were examined. In [PERSON] and [PERSON] [20], the segmentation of the continuous speech into smaller speech units was performed, and every unit was classified as a consonant or vowel by utilizing the formant frequencies and support vector machines (SVMs). It is reported that the Gaussian kernel yielded higher accuracy in comparison with the other kernels utilized. In [PERSON] [21], differences between vowels and consonants and between speech and nonspeeh with regard to the context range in which they are set were explained. Identification, discrimination, and delayed recognition tests, in which the discrimination of vowels and consonants was performed at various levels, were explained regarding the higher range of the vowel series over the consonant series and the tasks' memory demands. In [PERSON] et al. [22], two tests with vowels and consonants as the targets of phoneme detection in real words were performed. In the first test, the comparison of two comparatively distinct vowels with two confusable stop consonants was performed.