  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.
