One sentence voice adaptation using GMM-based frequency-warping and shift with a sub-band basis spectrum model

This paper presents a rapid voice adaptation algorithm using GMM-based frequency warping and shift with parameters of a sub band basis spectrum model (SBM). The SBM parameter represents a shape of a spectrum of speech. It is calculated by fitting a sub-band basis to the log-spectrum. Since the parameter is the frequency domain representation, frequency warping can be directly applied to the SBM parameter. A frequency warping function that minimize the distance between source and target SBM parameter pairs in each mixture component of a GMM is derived using a DP (Dynamic programming) algorithm. The proposed method is evaluated in an unit-selection based voice adaptation framework applied to a unit-fusion based text-to-speech synthesizer. The experimental results show that the proposed adaptation method is effective for rapid voice adaptation using just one sentence, compared to the conventional GMM.-based linear transformation of mel-cepstra.