Maximum Likelihood Parameter Generation (MLPG) [1,2] implementation in C for Python
pip install mlpg_c
from mlpg_c import mlpg_c as mlpg
param_gen = mlpg.mlpg_solve(jnt_sd_mat, prec_vec, coef_vec)
param_gen: generated parameter trajectory: T x dim
jnt_sd_mat: joint static-delta feature vector sequence: T x (dim*2)
prec_vec: vector of diagonal precision (inverse covariance) matrix: (dim*2) x 1
coef_vec: vector of delta coefficients: n_coeff x 1. e.g.: [-0.5,0.5,0.0]
from mlpg_c import mlpg_c as mlpg
param_gen = mlpg.mlpg_solve_seq(jnt_sd_mat, prec_mat, coef_vec)
param_gen: generated parameter trajectory: T x dim
jnt_sd_mat: joint static-delta feature vector sequence: T x (dim*2)
prec_mat: sequence of diagonal precision (inverse covariance) matrices: T x (dim*2)
coef_vec: vector of delta coefficients: n_coeff x 1, e.g.: [-0.5,0.5,0.0]
- full precision matrix
- docs
[1] K. Tokuda, T. Kobayashi, S. Imai, Speech parameter generation from HMM using dynamic features, in Proc. ICASSP, Detroit, USA, May 1995, pp. 660––663.
[2] T. Toda, A. W. Black, K. Tokuda, Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory, IEEE Trans. Audio Speech Lang. Process., vol. 15, no. 8, 2222-–2235, 2007.