Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training

TL;DR

This paper presents a novel end-to-end transformer-based TTS model for Kurdish Sorani dialect, combining VAE pre-training, adversarial training, and stochastic duration prediction to produce high-quality, natural speech with diverse rhythms.

Contribution

It introduces a new end-to-end TTS framework for Kurdish that leverages VAE, adversarial training, and stochastic duration modeling, addressing underrepresentation and quality issues.

Findings

Achieved a mean opinion score of 3.94, outperforming existing systems.

Demonstrated real-time synthesis with natural pitch and rhythm variation.

Validated effectiveness through subjective human evaluation.

Abstract

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.