RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis

By Rohola Zandie, Mohammad H. Mahoor, J. Madsen, Eshrat S. Emamian. 2021

30 citations recorded by the source corpus.

This paper introduces RyanSpeech, a new speech corpus for research on automated text-to-speech (TTS) systems. Publicly available TTS corpora are often noisy, recorded with multiple speakers, or lack quality male speech data. In order to meet the need for a high quality, publicly available male speech corpus within the field of speech recognition, we have designed and created RyanSpeech which contains textual materials from real-world conversational settings. These materials contain over 10 hours of a professional male voice actor's speech recorded at 44.1 kHz. This corpus's design and pipeline make RyanSpeech ideal for developing TTS systems in real-world applications. To provide a baseline for future research, protocols, and benchmarks, we trained 4 state-of-the-art speech models and a vocoder on RyanSpeech. The results show 3.36 in mean opinion scores (MOS) in our best model. We have made both the corpus and trained models for public use.

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, neutral): “1 English TED-LIUM [289] 118 666 / English CALLHOME [31] 60 120 8 English RyanSpeech [414] 10 1 44.”
• Machine Learning for Synthetic Data Generation: a Review (2023, unknown): “Genomes Project and HGDP datasets synthpop [29] healthcare proper synthesis Statistical hypothesis testing SD2011 CycleGAN [30] vision GAN CNN pix2pix DP-CGAN [31] vision GAN deep CGAN MNIST BigGANs [32] vision GAN large scale GAN ImageNet VideoDiff [33] vision diffusion CNN BAIR Robot Pushing, Kine”