GPU-Friendly Local Regression for Voice Conversion

Abstract

Voice conversion is the task of transforming a source speaker’s voice so that it sounds like a target speaker’s voice. We present a GPUfriendly local regression model for voice conversion that is capable of converting speech in real-time and achieves state-of-the-art accuracy on this task. Our model uses a new approximation for computing local regression coefficients that is explicitly designed to preserve memory locality. As a result, our inference procedure is amenable to efficient implementation on the GPU. Our approach is more than 10X faster than a highly optimized CPUbased implementation, and is able to convert speech 2.7X faster than real-time.