A multivariate voicing decision rule adapts to noise, distortion, and spectral shaping

A new approach to making voiced/unvoiced decisions is presented. The technique is very accurate and dynamically adapts to a wide range of environments. Reliable decisions are achieved by using a weighted sum of multiple speech parameters. Instead of using discriminant analysis to determine the optimal weights, voiced and unvoiced frames are separated into two clusters by a multivariate clustering algorithm. Since cluster analysis requires no prior voicing information, the decision rule is computed from the incoming speech rather than from a training set. An adaptive clustering algorithm is derived which continuously adjusts the weights in response to changing speech characteristics.