Stochastic modeling of spectral adjustment for high quality pitch modification

We present a new algorithm for adjusting the magnitude spectrum when the fundamental frequency (F0) of a speech signal is altered. The algorithm exploits the correlation between F0 and the magnitude spectrum of speech as represented by line spectral frequencies (LSFs). This correlation is class-dependent, and thus a broad classification of the input is achieved by a Gaussian mixture model (GMM). The within-class dependencies of LSFs on F0 values are captured by constructing their joint probability densities using a series of GMMs, one for each speech class. The proposed system is used for post-processing the pitch modified signal. Perceptual tests showed that the addition of this post-processing system improves the naturalness of the pitch modified signal for large pitch modification factors.