Rapid Connectionist Speaker Adaptation

• URL: http://arxiv.org/abs/2211.08978v1
• Date: Tue, 15 Nov 2022 00:15:11 GMT
• Title: Rapid Connectionist Speaker Adaptation
• Authors: Michael Witbrock and Patrick Haffner
• Abstract summary: We present SVCnet, a system for modelling speaker variability. Nerve Networks specialized for each speech sound produce low dimensionality models of acoustical variation. A training procedure is described which minimizes the dependence of this model on which sounds have been uttered.
• Score: 3.00476084358666
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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