Patent ID: 7653535
Filing Date: 2010-01-26
Classification: G10L

Abstract:
1. A method comprising: implementing a likelihood function using a processor, the likelihood function for a sequence of LPC cepstra observation vectors being utilized to estimate a mean for a distribution of target vocal tract resonances where K is the number of frames in the training signal, J is the number of orders in the LPC cepstra observation vectors o k , μ o (j)=F n [z 0 (k)]+F n ′[z 0 (k)][α k μ T −Z 0 (k)]+μ r and σ o (j) is the jth element along the diagonal matrix: Σ o =Σ r +F n ′[z 0 (k)]Σ z (k)(F n ′[z 0 (k)]) Tr where Σ r is a covariance of a residual model, Σ z (k) is a covariance of a distribution of vocal tract resonance trajectories, is for an nth LPC cepstral order for a Taylor series expansion point z 0 (k), f s is a sampling frequency of a speech signal, f p is a vocal tract resonance frequency and b p is a corresponding vocal tract resonance bandwidth, μ T is a vector of means of target vocal tract resonances, α k is a filter parameter vector, μ r is a mean of the residual model, F n ′[z 0 (k)] is the derivative of F n [z 0 (k)] with respect to a vocal tract resonance dimension, wherein utilizing a likelihood function comprises: for each combination of a speech unit l o and a dimension f 0 that can be formed from a plurality of speech units and a multi-dimensional target vocal tract resonance vector, each partial derivative being taken with respect to a dimension of a mean target vocal tract resonance vector for a speech unit; setting each of the plurality of partial derivatives equal to zero to form a system of equations with each equation in the system having a different combination of (f o , l 0 ); and