# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 1 Introduction  
In recent years, text-to-speech (TTS) systems have undergone significant advancements, which were largely driven by the adoption of deep learning (DL) techniques. These systems transform written text into natural-sounding speech through a multi-stage process. However, traditional approaches often necessitate trade-offs between the quality of the synthesized speech, the speed of generation, and the complexity of the training process. One prevalent approach leverages a two-stage architecture [1, 2]. In the initial stage, the system generates intermediate representations, such as linguistic features [2] or mel-spectograms--a representation capturing the frequency content of an audio signal over time [1] from the preprocessed text. To generate mel-spectograms from the preprocessed text, the system typically utilizes techniques like text-to-speech (TTS) models that convert the text into spectrogram images. These spectrograms represent how the frequency content of the audio signal varies over time, providing a structured format for subsequent stages of audio synthesis. The second stage then translates these representations into raw audio waveforms [2, 3]. While this method has yielded realistic speech, it suffers from limitations. Training these models often requires sequential training or fine-tuning, hindering efficiency. Additionally, their reliance on predefined intermediate features restricts the utilization of potentially beneficial learned representations, limiting the system's ability to further improve performance. To address these shortcomings, alternative approaches have been explored, such as non-autoregressive methods [4; 5] and generative adversarial networks (GANs) [6]. Non-autoregressive models aim to overcome the slow generation speed inherent to autoregressive systems such as Tacotron 2 [1] and Transformer TTS [7] by eliminating the sequential nature of the process. These models can synthesize speech significantly faster, making them more suitable for real-time applications. On the other hand, GAN-based methods have shown promise in generating high-quality waveforms, potentially surpassing the quality achieved by two-stage approaches [8; 9]. Recent efforts have focused on developing efficient end-to-end training methods for TTS models [10; 11]. These methods aim to bypass the two-stage pipeline entirely and directly convert text into speech. While these approaches offer potential performance improvements by leveraging learned representations throughout the entire process, they often fall short of the quality achieved by two-stage systems, highlighting the ongoing challenge of balancing efficiency and quality in TTS. The Sorani Kurdish dialect is predominantly spoken by Kurdish communities in Iraq and Iran, representing a significant linguistic and cultural identity within these regions. Despite its importance, Sorani Kurdish has faced challenges in modern technological advancements, particularly in the realm of TTS conversion. TTS technology plays a crucial role in enhancing accessibility and inclusivity for languages and dialects worldwide. However, the development of TTS systems for Sorani Kurdish has been relatively limited compared to more widely spoken languages. This discrepancy poses a barrier to the full participation of Sorani Kurdish speakers in digital communication, education, and accessibility tools that rely on TTS technology. This study presents a parallel end-to-end TTS method to address the limitations of both traditional two-stage architectures and recent end-to-end methods. The proposed model is named Kurdish TTS (KTTS), as it will be developed to generate more natural-sounding Central Kurdish audio than that generated by current two-stage models. The goal of this study is to achieve high-quality speech synthesis while maintaining efficiency and simplified training protocols. Our approach utilizes a variational autoencoder (VAE) that is pre-trained for audio waveform reconstruction.