# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 7 Conclusions  
This study introduced KTTS, an efficient end-to-end text-to-speech model tailored for generating high-quality Kurdish audio. By leveraging a pre-trained VAE for audio waveform reconstruction and integrating adversarial training techniques, we enhanced the expressiveness while ensuring high-fidelity speech synthesis. Our methodology effectively bridges the text-to-speech gap by aligning prior and posterior distributions within latent variables. An empirical evaluation on a custom dataset demonstrated KTTS's superior performance, which was comparable to the ground truth and was validated through subjective human evaluation. This represents a significant advancement in TTS technology, offering efficiency, quality, and flexibility for the synthesis of Kurdish text and speech. Future work may focus on enhancing the model further and expanding its applicability to other Kurdish dialects, speech styles, and multi-speaker models. Another area for future work involves integrating the two training phases of the VAE and KTTS into a unified  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Latency (s)** & **Speedup (\%)** \\ \hline One-Stage Systems & & \\ \hline VITS & 0.560 \(\pm\) 0.068 & / \\ KTTS & 0.517 \(\pm\) 0.093 & 8.32 \\ \hline Two-Stage Systems & & \\ \hline Tacotron 2 (Mel + multi-band MelGAN) & 0.795 \(\pm\) 0.011 & / \\ KTTS & 0.517 \(\pm\) 0.093 & 53.73 \\ \hline Glow TTS (Mel + multi-band MelGAN) & 0.763 \(\pm\) 0.044 & / \\ KTTS & 0.517 \(\pm\) 0.093 & 47.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of the inference speed with 95% confidence intervals.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **RTF (CI)** \\ \hline Tacotron 2 (Mel + multi-band MelGAN) & 0.099 \(\pm\) 0.006 \\ \hline Glow TTS (Mel + multi-band MelGAN) & 0.095 \(\pm\) 0.011 \\ \hline VITS & 0.0701 \(\pm\) 0.006 \\ \hline
**KTTS** & **0.065 \(\pm\) 0.004** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of RTF with 95% confidence intervals.  
procedure, enabling seamless integration without the requirement to pre-train the VAE network's encoder and decoder separately.  
H.A.A.: Data curation, original draft preparation, visualization, conceptualization, methodology, software, validation, resources, writing, reviewing and editing. T.A.R.: supervision, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data is accessible via the Mendeley Data repository at the following URL: [[https://data.mendeley.com/datasets/zhnvwsd7](https://data.mendeley.com/datasets/zhnvwsd7) hs/1]([https://data.mendeley.com/datasets/zhnvwsd7](https://data.mendeley.com/datasets/zhnvwsd7) hs/1) (accessed on 18 June 2024).  
We would like to acknowledge [PERSON] for his invaluable assistance in preparing the server with two NVIDIA GPUs for our development process.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
TTS Text to speech  
MOS Mean opinion score  
VAE Variational autoencoder  
GANs Generative adversarial networks  
KTTS Kurdish text to speech