Enhancing Kurdish Text-to-Speech with Native Corpus Training: A High-Quality WaveGlow Vocoder Approach
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Abstract (arXiv)
The ability to synthesize spoken language from text has greatly facilitated access to digital content with the advances in text-to-speech technology. However, effective TTS development for low-resource languages, such as Central Kurdish (CKB), still faces many challenges due mainly to the lack of linguistic information and dedicated resources. In this paper, we improve the Kurdish TTS system based on Tacotron by training the Kurdish WaveGlow vocoder on a 21-hour central Kurdish speech corpus instead of using a pre-trained English vocoder WaveGlow. Vocoder training on the target language corpus is required to accurately and fluently adapt phonetic and prosodic changes in Kurdish language. The effectiveness of these enhancements is that our model is significantly better than the baseline system with English pretrained models. In particular, our adaptive WaveGlow model achieves an impressive MOS of 4.91, which sets a new benchmark for Kurdish speech synthesis. On one hand, this study empowers the advanced features of the TTS system for Central Kurdish, and on the other hand, it opens the doors for other dialects in Kurdish and other related languages to further develop.
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References · 11
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
- 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 752
- 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 309
- 2022NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Qualitycited by 152
- 2017Deep Voice: Real-time Neural Text-to-Speechcited by 83
- 2019WaveFlow: A Compact Flow-based Model for Raw Audiocited by 51
- 2022Towards Building Text-To-Speech Systems for the Next Billion Userscited by 27
- 2020StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalizationcited by 26
- 2024Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigmcited by 7
- 2020Ultrasound-based Articulatory-to-Acoustic Mapping with WaveGlow Speech Synthesiscited by 4
- 2019RNN-based speech synthesis using a continuous sinusoidal modelcited by 1
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