Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 24 references
Abstract
When fine-tuning large language models, the assumption is that more data means better results. This principle is often extended to text-to-speech (TTS) fine-tuning, yet remains underexplored, particularly for low-resource languages where high-quality data is difficult to obtain. In this work, the "more data is better" phenomenon is investigated for Turkish TTS using XTTS v2 by incrementally increasing training data. Speech quality is evaluated using multiple metrics, including UTMOS, NISQA, and an LLM-based TTS evaluation framework. For the LLM-based evaluation, a multimodal language model (Gemini) was prompted to assess Turkish-specific pronunciation, naturalness, and synthesis artifacts on a calibrated 1-10 scale. Results suggest that for TTS fine-tuning on non-mainstream languages, modest data investments may achieve near-optimal quality.
Findings indicate that language-specific fine-tuning plays a more critical role than multilingual generalization in achieving accurate ASR for Indonesian and provide practical guidance for deploying ASR systems in low-resource language scenarios.
J. Hebert, Amalia Zahra· Bulletin of Electrical Engin...· 0 citations
Text-to-speech systems are improving fast, but measuring how natural they sound still requires expensive human listening tests. Existing automatic methods struggle to generalise well across different datasets. We present QMOS, a MOS prediction framework that extracts hierarchical speech quality features from a frozen Q...
Ravi Sastry Kolluru, S. Devarakonda, S. Radhe Shyam Salopanthula et al.· International Conference on...· 0 citations
This is the first systematic study of Singlish-accented ZS-TTS, and fine-tuning raises accent similarity on in-domain and out-of-domain speakers for both Chatterbox and CosyVoice 3.
Developing text-to-speech (TTS) systems for a language with limited accessible speech data such as Turkish remains a challenge. This study describes a process for creating a Turkish text-to-speech system using web-scraping data to train deep learning models. The data collection approach is based on transcribing Turkish...
Hüseyin Çakmak, Kuzey Arar, F. B. Tek· Signal Processing and Commun...· 0 citations
It is suggested that many reported downstream gains reflect instance and seed dependent elicitation match, rather than universally improving the attainable performance ceiling, in self-supervised fine-tuning.
Wangjin Zhou, Yizhou Zhang, Yichi Wang et al.· 0 citations
It is demonstrated that increasing manually reviewed transcriptions consistently improves word and character error rates, while audio enhancement slightly reduces performance, highlighting the complex trade-offs in low-resource ASR development.
Olga Kriukova, O. Lovick, Antti Arppe· Proceedings of the Sixth Wor...· 0 citations
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