TY - GEN
T1 - Fitting new speakers based on a short untranscribed sample
AU - Nachmani, Eliya
AU - Polyak, Adam
AU - Taigman, Yaniv
AU - Wolf, Lior
N1 - Publisher Copyright:
© The Author(s) 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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 cur-rently 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 im-proved performance on both the dataset speakers, and, more importantly, when fitting new voices, even from very short samples.
AB - 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 cur-rently 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 im-proved performance on both the dataset speakers, and, more importantly, when fitting new voices, even from very short samples.
UR - http://www.scopus.com/inward/record.url?scp=85057261590&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85057261590
T3 - 35th International Conference on Machine Learning, ICML 2018
SP - 5932
EP - 5940
BT - 35th International Conference on Machine Learning, ICML 2018
A2 - Dy, Jennifer
A2 - Krause, Andreas
PB - International Machine Learning Society (IMLS)
T2 - 35th International Conference on Machine Learning, ICML 2018
Y2 - 10 July 2018 through 15 July 2018
ER -