Speech analysis with blind equalization technique

The conventional linear prediction can be viewed as a constrained blind equalization problem that has gained a lot of interests along with development of telecommunication networks. Because the blind equalization or deconvolution is a general framework of the inverse problem, the reliable and faster algorithm is requested in many applications. This paper proposes an orthogonal wavelet transform domain realization of a blind equalization technique termed as EVA, and presents an application to speech analysis. An orthogonal transformation has no influence to the equalization result in general, but we show that a particular wavelet makes the matrix in EVA nearly lower triangular that promotes the faster convergence in the estimation of maximum eigenvalue and its associate vector in EVA iteration. The experiments with the Japanese vowels show that the the proposed method effectively separates the glottis and vocal tract information! hence is promising for speech analysis.