Rapid Connectionist Speaker Adaptation
Sound
2022-11-17 v1 Artificial Intelligence
Audio and Speech Processing
Abstract
We present SVCnet, a system for modelling speaker variability. Encoder Neural Networks specialized for each speech sound produce low dimensionality models of acoustical variation, and these models are further combined into an overall model of voice variability. A training procedure is described which minimizes the dependence of this model on which sounds have been uttered. Using the trained model (SVCnet) and a brief, unconstrained sample of a new speaker's voice, the system produces a Speaker Voice Code that can be used to adapt a recognition system to the new speaker without retraining. A system which combines SVCnet with an MS-TDNN recognizer is described
Cite
@article{arxiv.2211.08978,
title = {Rapid Connectionist Speaker Adaptation},
author = {Michael Witbrock and Patrick Haffner},
journal= {arXiv preprint arXiv:2211.08978},
year = {2022}
}
Comments
6 Figures, Two Tables, ICASSP-92