## 7 Conclusions  
In this article, we presented the development of a speaker identification system using deep neural networks by proposing novel features to evaluate the system. The features that we considered were pitch, energy, MFCCs, and their first and second derivatives. To design our system, we considered a public English speech corpus, the YOHO dataset managed and published by LDC, with 108 males and 32 females. The proposed system  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Dataset** & **Features** & **Technique** & **Recognition** \\  & & & & & **Accuracy Rate (\%)** \\ \hline
[18] & 2020 & - & MFCCs and Pitch & Correlation & 92.00 \\ \hline
[34] & 2021 & TIMIT & MFCCT & ANN & 92.90 \\ \hline
[11] & 2021 & VoxCeleb1 & Spectrogram & VGG ResNet & 93.80 \\ \hline
[12] & 2023 & - & MFCCs & CNN & 92.46 \\ \hline Our Proposed Features & & & & & \\  & & & &  
was developed with several deep learning approaches, such as MLP, LSTM, and BLSTM. In the first experiment, we created a simple MLP model with ten speakers, and the accuracy reached 88.90%. The limitations of MLP led us to consider other approaches. The speech signal is a quasi-stationary and time-varying signal; we require more understanding of these characteristics. The RNN approaches are known as time-series neural network models. Based on considerable research, LSTM and BLSTM are suitable models for such systems.  
In the second experiment, we used the LSTM model, which provided an accuracy of 85.22%. The last experiment was BLSTM; the system reached an overall average accuracy of 91.93%. The last model expressed five runs to compare the effect of the speech samples that were selected randomly for each run. The results of all experiments were analyzed, and the errors were considered. A perceptual test was performed, and statistical analysis was used to measure the effectiveness of the speaker's audio file's direct impact on the quality of the designed classification model. The BLSTM model used to evaluate the speaker identification system provided the highest accuracy, and for the proposed features, it may require more enhancement.  
Future research that could build on this work involves using the proposed features in other system types, such as speaker verification or gender and age recognition. The proposed speaker identification system could be improved by adding extra features such as a spectrogram or GTCC. It would also be interesting to express the developed model in different datasets and languages to compare language's impact on speech processing systems. Furthermore, the research could be developed using different approaches to deep neural networks, such as CNN.  
Conceptualization, N.M.A., A.A.A. and Y.A.A.; methodology, N.M.A. and Y.A.A.; software, N.M.A.; validation, N.M.A. and Y.A.A.; formal analysis, N.M.A., A.A.A. and Y.A.A.; investigation, N.M.A., A.A.A. and Y.A.A.; resources, N.M.A. and Y.A.A.; data curation, N.M.A.; writing--original draft preparation, A.A.A.; writing--review and editing, N.M.A., A.A.A. and Y.A.A.; visualization, A.A.A.; supervision, Y.A.A.; project administration, A.A.A.; funding acquisition, Y.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Researchers Supporting Project number (RSP-2022/322), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors would like to thank the Researchers Supporting Project at King Saud University.  
The authors declare that there are no conflict of interest regarding the publication of this article.