mlsanityData to 14 Oct 2026

Fitting New Speakers Based on a Short Untranscribed Sample

arXiv 1802.06984 ↗First posted 20 Feb 2018cs.LG cs.SD eess.AScited by 18

Not on the map. Why?

Abstract (arXiv)

Learning-based Text To Speech systems have the potential to generalize from one speaker to the next and thus require a relatively short sample of any new voice. However, this promise is currently largely unrealized. We present a method that is designed to capture a new speaker from a short untranscribed audio sample. This is done by employing an additional network that given an audio sample, places the speaker in the embedding space. This network is trained as part of the speech synthesis system using various consistency losses. Our results demonstrate a greatly improved performance on both the dataset speakers, and, more importantly, when fitting new voices, even from very short samples.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Feb 2018.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 8; 2022: 7; 2023: 2; 2024: 1.

18 papers.

Sort:

By area: