Enhancing Kurdish Text-to-Speech with Native Corpus Training: A High-Quality WaveGlow Vocoder Approach

This study introduces the first TTS vocoder based on 21 hours of detailed Kurdish speech data, significantly advancing Kurdish language technology. The researchers successfully adapted the WaveGlow deep learning architecture to Kurdish, optimizing it for the unique acoustic properties of the language to ensure clear, natural speech output. Advanced prosody modeling techniques were also implemented to improve the rhythm, stress, and intonation of the synthesized speech, crucial for achieving lifelike speech quality.