  Recent advancements in text-to-speech (TTS) models have aimed to streamline
the two-stage process into a single-stage training approach. However, many
single-stage models still lag behind in audio quality, particularly when
handling Kurdish text and speech. There is a critical need to enhance
text-to-speech conversion for the Kurdish language, particularly for the Sorani
dialect, which has been relatively neglected and is underrepresented in recent
text-to-speech advancements. This study introduces an end-to-end TTS model for
efficiently generating high-quality Kurdish audio. The proposed method
leverages a variational autoencoder (VAE) that is pre-trained for audio
waveform reconstruction and is augmented by adversarial training. This involves
aligning the prior distribution established by the pre-trained encoder with the
posterior distribution of the text encoder within latent variables.
Additionally, a stochastic duration predictor is incorporated to imbue
synthesized Kurdish speech with diverse rhythms. By aligning latent
distributions and integrating the stochastic duration predictor, the proposed
method facilitates the real-time generation of natural Kurdish speech audio,
offering flexibility in pitches and rhythms. Empirical evaluation via the mean
opinion score (MOS) on a custom dataset confirms the superior performance of
our approach (MOS of 3.94) compared with that of a one-stage system and other
two-staged systems as assessed through a subjective human evaluation.
