Speech synthesis using warped linear prediction and neural networks

A text-to-speech synthesis technique, based on warped linear prediction (WLP) and neural networks, is presented for high-quality individual sounding synthetic speech. Warped linear prediction is used as a speech production model with wide audio bandwidth yet with highly compressed control parameter data. An excitation codebook, inverse filtered from a target speaker’s voice, is applied to obtain individual tone quality. A set of neural networks, specialized to yield synthesis control parameters from phonemic input in specific contexts, generate the detailed parametric controls of WLP. Neural nets are also used successfully to compute the prosodic parameters. We have applied this approach in prototyping highly improved text-to-speech synthesis for the Finnish language.