公开(公告)号：CA2290185C

公开(公告)日：2005-09-20

申请号：CA2290185

申请日：1999-11-22

Applicant: IBM

Inventor： BASU SANKAR ,  MAES STEPHANE H

IPC: G10L15/10 ,  G10L15/02 ,  G10L15/06 ,  G10L15/20 ,  G10L21/02 ,  G10L19/02

Abstract: Systems and methods for processing acoustic speech signals which utilize the wavelet transform (and alternatively, the Fourier transform) as a fundamental tool. The method essentially involves "synchrosqueezing" spectral component data obtained by performing a wavelet transform (or Fourier transform) on digitized speech signals. In one aspect, spectral components of the synchrosqueezed plane are dynamically tracked via a K-means clustering algorithm. The amplitude, frequency and bandwidth of each of the; components are, thus, extracted. The cepstrum generated from this information is referred to as "K-mean Wastrum." In another aspect, the result of the K-mean clustering process is further processed to limit the set of primary components to formants. The resulting features are referred to as "formant-based wastrum." Formants are interpolated in unvoiced regions and the contribution of unvoiced turbulent part of the spectrum are added. This method requires adequate formant tracking. The resulting robust formant extraction has a number of applications in speech processing and analysis including vocal tract normalization.

公开(公告)号：GB2362744B

公开(公告)日：2004-03-10

申请号：GB0111902

申请日：2001-05-16

Applicant: IBM

Inventor： BASU SANKAR ,  MICCHELLI CHARLES A ,  OLSEN PEDER

IPC: G10L15/02

Abstract: Improvements in speech recognition systems are achieved by considering projections of the high dimensional data on lower dimensional subspaces, subsequently by estimating the univariate probability densities via known univariate techniques, and then by reconstructing the density in the original higher dimensional space from the collection of univariate densities so obtained. The reconstructed density is by no means unique unless further restrictions on the estimated density are imposed. The variety of choices of candidate univariate densities as well as the choices of subspaces on which to project the data including their number further add to this non-uniqueness. Probability density functions are then considered that maximize certain optimality criterion as a solution to this problem. Specifically, those probability density function's that either maximize the entropy functional, or alternatively, the likelihood associated with the data are considered.

公开(公告)号：GB2362744A

公开(公告)日：2001-11-28

申请号：GB0111902

申请日：2001-05-16

Applicant: IBM

Inventor： BASU SANKAR ,  MICCHELLI CHARLES A ,  OLSEN PEDER

IPC: G10L15/02

Abstract: Improvements in speech recognition systems are achieved by considering projections of the high dimensional data on lower dimensional subspaces, subsequently by estimating the univariate probability densities via known univariate techniques, and then by reconstructing the density in the original higher dimensional space from the collection of univariate densities so obtained. The reconstructed density is by no means unique unless further restrictions on the estimated density are imposed. The variety of choices of candidate univariate densities as well as the choices of subspaces on which to project the data including their number further add to this non-uniqueness. Probability density functions are then considered that maximize certain optimality criterion as a solution to this problem. Specifically, those probability density functions that either maximize the entropy functional, or alternatively, the likelihood associated with the data are considered.

公开(公告)号：CA2290185A1

公开(公告)日：2000-05-30

申请号：CA2290185

申请日：1999-11-22

Applicant: IBM

Inventor： BASU SANKAR ,  MAES STEPHANE H

IPC: G10L15/10 ,  G10L15/02 ,  G10L15/06 ,  G10L15/20 ,  G10L21/02 ,  G10L19/02

Abstract: Systems and methods for processing acoustic speech signals which utilize the wavelet transform (and alternatively, the Fourier transform) as a fundamental tool. The method essentially involves "synchrosqueezing" spectral component data obtained by performing a wavelet transform (or Fourier transform) on digitized speech signals. In one aspect, spectral components of the synchrosqueezed plane are dynamically tracked via a K-means clustering algorithm. The amplitude, frequency and bandwidth of each of the components are, thus, extracted. The cepstrum generated from this information is referred to as "K-mean Wastrum." In another aspect, the result of the K-mean clustering process is further processed to limit the set of primary components to formants. The resulting features are referred to as "formant-based wastrum." Formants are interpolated in unvoiced regions and the contribution of unvoiced turbulent part of the spectrum are added. This method requires adequate formant tracking. The resulting robust formant extraction has a number of applications in speech processing and analysis including vocal tract normalization.