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Data-driven global boundary optimization

  • US 7,409,347 B1
  • Filed: 10/23/2003
  • Issued: 08/05/2008
  • Est. Priority Date: 10/23/2003
  • Status: Expired due to Fees
First Claim
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1. A machine-implemented method comprising:

  • extracting portions from segment boundary region of a plurality of speech segments, each segment boundary region based on a corresponding initial unit boundary;

    creating feature vectors that represent the portions in a vector space;

    for each of a plurality of potential unit boundaries within each segment boundary region, determining an average discontinuity based on distances between the feature vectors; and

    for each segment, selecting the potential unit boundary associated with a minimum average discontinuity as a new unit boundary;

    wherein the portions include centered pitch periods, the centered pitch periods derived from pitch periods of the segments, wherein the feature vectors incorporate phase information of the portions, wherein creating feature vectors comprises;

    constructing a matrix W from the portions; and

    decomposing the matrix W, andwherein the matrix W is a (2(K−

    1)+1)M×

    N matrix represented by W=UΣ

    VT where K−

    1 is the number of centered pitch periods near the potential unit boundary extracted from each segment, N is the maximum number of samples among the centered pitch periods, M is the number of segments, U is the (2(K−

    1)+1)M×

    R left singular matrix with row vectors ui(1≦

    i≦

    (2(K−

    1)+1)M), Σ

    is the R×

    R diagonal matrix of singular values s1

    s2

    . . . ≧

    sR>

    0, V is the N×

    R right singular matrix with row vectors vj(1≦

    j≦

    N), R<

    <

    (2(K−

    1)+1)M), and T denotes matrix transposition, wherein decomposing the matrix W comprises performing a singular value decomposition of W.

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