GPU-Friendly Local Regression for Voice Conversion
Taylor Berg-Kirkpatrick, Dan Klein · 2015
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.