Generating Gender-Ambiguous Text-to-Speech Voices

The gender of a voice assistant or any voice user interface is a central element of its perceived identity. While a female voice is a common choice, there is an increasing interest in alternative approaches where the gender is ambiguous rather than clearly identifying as female or male. This work addresses the task of generating gender-ambiguous text-to-speech (TTS) voices that do not correspond to any existing person. This is accomplished by sampling from a latent speaker embeddings' space that was formed while training a multilingual, multi-speaker TTS system on data from multiple male and female speakers. Various options are investigated regarding the sampling process. In our experiments, the effects of different sampling choices on the gender ambiguity and the naturalness of the resulting voices are evaluated. The proposed method is shown able to efficiently generate novel speakers that are superior to a baseline averaged speaker embedding. To our knowledge, this is the first systematic approach that can reliably generate a range of gender-ambiguous voices to meet diverse user requirements.