Synthesized Spoken Names: Biases Impacting Perception

Utilizing a existing neural text-to-speech synthesis architecture to generate person names and comparing them to reference names read aloud in a formal context, we explore how bias resulting from training data impacts the synthesis of person names, focusing on frequency and origin of names. Long-term, we aim to apply voice conversion of person names to aid the effective reading aloud of such names in celebratory ceremonies.