Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 13 references
Abstract
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 audiobook content from YouTube and converting it into a usable dataset using normalization, piece segmentation, and human annotation methods. In this study, the performances of fine-tuning KaniTTS and Dia voice models are compared with the performance of Elevenlabs voice clone. It has been observed that fine-tuned voice models with limited resources gained the ability to synthesize at the level of commercial based API voice model.
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
This work constructed a large-scale Arabic Speech Question-Answering (SQA) corpus comprising over 1.5 million training samples to allow instruction tuning over a broad range of core speech tasks and designed an evaluation framework featuring diverse tasks and tailored metrics.
Yingzhi Wang, Reem S. Alhazzani, Muhammad Alqurishi· 0 citations
DeepL Voice achieves competitive transcription quality through proprietary real-time ASR models and eliminates translation "flickering" via stable text streaming while maintaining low latency.
Johannes Ernesti, Peter Kaiser, Jonas Heinze et al.· Proceedings of the Thirty-Fi...· 0 citations
Online voice-based applications and speech communication have grown as a result of the revolutionary rise of smart gadgets and social media. The rapid advancement of deep learning (DL) has transformed the field of audio processing, enabling smooth human-computer interaction. DL approaches have been used to develop spee...
Muzaffar Ahmad Dar, Sri Gani Kaarthikeya Kammula, J. Pushparaj et al.· Frontiers in Artificial Inte...· 0 citations
This paper investigates the effectiveness of pre-trained models like Wav2Vec XLSR-53 and Whisper-Small for developing ASR systems for the Telugu language, addressing the challenge of limited data availability and demonstrating satisfactory results even when fine-tuned on a smaller dataset.
J. Pushparaj, Muzaffar Ahmad Dar, Sri Gani Kaarthikeya Kammula et al.· Frontiers in Artificial Inte...· 0 citations
Experimental results show that VALL-E outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity and could preserve the speaker’s emotion and acoustic environment from the prompt in synthesis.
Unknown authors· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
