Acoustic model creating method, acoustic model creating apparatus, acoustic model creating program, and speech recognition apparatus

• US 20050131694A1
• Filed: 11/29/2004
• Published: 06/16/2005
• Est. Priority Date: 12/12/2003
• Status: Abandoned Application

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Matsumoto, Hiroshi, Yamamoto, Kazumasa, Miyazawa, Yasunaga, Nishitani, Masanobu
• Application Number:
  US10/998,065
• Publication Number:
  US 20050131694A1
• Time in Patent Office: Days
• US Class Current: 704/256
• CPC Class Codes: G10L 15/144 Training of HMMs

Abstract

Exemplary embodiments of the invention enhance the recognition ability by optimizing the distribution numbers for respective states that constitute an HMM (for example, a syllable HMM). Exemplary embodiments provide a distribution number setting device to increment the distribution number step by step for each state in an HMM; an alignment data creating unit to create alignment data by matching each state having been set to a specific distribution number to training speech data; a description length calculating unit to find, according to the Minimum Description Length criterion, a description length of each state in an HMM having the present time distribution number and a description length of each state in an HMM having the immediately preceding distribution number, with the use of the alignment data; and an optimum distribution number determining device to set an optimum distribution number to each state on the basis of the size of the description length found for each state in the HMM having the present time distribution number and the description length found for each state in the HMM having the immediately preceding distribution number.

Claims

1. An acoustic model creating method of optimizing Gaussian distribution numbers for respective states constituting an HMM (hidden Markov Model) for each state, and thereby creating an HMM having optimized Gaussian distribution numbers, the acoustic model creating method comprising:

incrementing a Gaussian distribution number step by step according to a specific increment rule for each state in plural HMM'"'"'s, and setting each state to a specific Gaussian distribution number;

creating matching data by matching each state in respective HMM'"'"'s, which has been set to the specific Gaussian distribution number in the distribution number setting, to training speech data;

finding, according to a Minimum Description Length criterion, a description length of each state in respective HMM'"'"'s having a Gaussian distribution number at a present time to be outputted as a present time description length, and finding, according to the Minimum Description Length criterion, a description length of each state in respective HMM'"'"'s having a Gaussian distribution number immediately preceding the present time to be outputted as an immediately preceding description length, with use of the matching data created in the matching data creating; and

comparing the present time description length with the immediately preceding description length in size, both of which are calculated in the description length calculating, and setting an optimum Gaussian distribution number for each state in respective HMM'"'"'s on a basis of a comparison result.

2. The acoustic model creating method according to claim 1,

according to the Minimum Description Length criterion, when a model set {1, i, . . . , I} and data χ

^N={χ

₁, . . . , χ

_N} (where N is a data length) are given, a description length li(χ

^N) using a model i being expressed by a general equation (1);

$\begin{array}{cc}{l}_i<br><br>\left(x^N\right)=-\log <br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\widehat{\mathrm{\theta <br><br>}}<br><br>\left(i\right)}<br><br>\left(x^N\right)+\frac{{\mathrm{\beta <br><br>}}_i}{2}<br><br>\log <br><br>\text{\hspace{1em}<br><br>}<br><br>N+\log <br><br>\text{\hspace{1em}<br><br>}<br><br>I& \left(1\right)\end{array}$

where {circumflex over (θ

)}(i) is a parameter of the model i, θ

⁽ⁱ⁾=θ

₁⁽ⁱ⁾, . . . , θ

<sub>β

i</sub>⁽ⁱ⁾ is a quantity of maximum likelihood estimation, and β

_i is a dimension (the number of free parameters) of the model i; and

in the general equation (1) to find the description length, let the model set {1, i, . . . , I} be a set of HMM'"'"'s when the distribution number for each state in the HMM is set to plural kinds from a given value to a maximum distribution number, then, given I kinds (I is an integer satisfying I≧

2) as the number of the kinds of the distribution number, 1, . . . , i, . . . , I are codes to specify respective kinds from a first kind to an I'"'"'th kind, and the equation (I) is used as an equation to find a description length of an HMM having the distribution number of an i'"'"'th kind among 1, . . . , i, . . . , I.

3. The acoustic model creating method according to claim 2,

an equation, in a re-written form of the equation (1), set forth below is used as an equation (2) to find the description length:

$\begin{array}{cc}{l}_i<br><br>\left(x^N\right)=-\log <br><br>\text{\hspace{1em}<br><br>}<br><br>P_{\widehat{\mathrm{\theta <br><br>}}<br><br>\left(i\right)}<br><br>\left(x^N\right)+\mathrm{\alpha <br><br>}<br><br>\left(\frac{{\mathrm{\beta <br><br>}}_i}{2}<br><br>\log <br><br>\text{\hspace{1em}<br><br>}<br><br>N\right)& \left(2\right)\end{array}$

where {circumflex over (θ

)}(i) is a parameter of a state i and θ

⁽ⁱ⁾=θ

₁⁽ⁱ⁾, . . . , θ

<sub>β

i</sub>⁽ⁱ⁾ is a quantity of maximum likelihood estimation.

4. The acoustic model creating method according to claim 3,

α

in the equation (2) being a weighting coefficient to obtain an optimum distribution number.

5. The acoustic model creating method according to claim 2

the data χ

^N being a set of respective pieces of training speech data obtained by matching, for each state in time series, HMM'"'"'s having an arbitrary distribution number among the given value through the maximum distribution number to many pieces of training speech data.

6. The acoustic model creating method according to claim 2

in the description length calculating, a total number of frames and a total likelihood being found for each state in respective HMM'"'"'s with the use of the matching data, for respective HMM'"'"'s having the present time Gaussian distribution number, and the present time description length being found by substituting the total number of frames and the total likelihood in Equation (2), while a total number of frames and a total likelihood are found for each state in respective HMM'"'"'s with the use of the matching data, for respective HMM'"'"'s having the immediately preceding Gaussian distribution number, and the immediately preceding description length being found by substituting the total number of frames and the total likelihood in Equation (2).

7. The acoustic model creating method according to claim 1

in the optimum distribution number determining, as a result of comparison of the present time description length with the immediately preceding description length, when the immediately preceding description length is smaller than the present time description length, the immediately preceding Gaussian distribution number being assumed to be an optimum distribution number for a state in question, and when the present time description length is smaller than the immediately preceding description length, the present time Gaussian distribution number being assumed to be a tentative optimum distribution number at this point in time for the state in question.

8. The acoustic model creating method according to claim 7,

in the distribution number setting, for the state judged as having the optimum distribution number, the Gaussian distribution number being held at the optimum distribution number, and for the state judged as having the tentative optimum distribution number, the Gaussian distribution number being incremented according to the specific increment rule.

9. The acoustic model creating method according to claim 6, further comprising, as processing prior to a description length calculation performed in the description length calculating

finding an average number of frames of a total number of frames of each state in respective HMM'"'"'s having the present time Gaussian distribution number and a total number of frames of each state in respective HMM'"'"'s having the immediately preceding Gaussian distribution number;

and

finding a normalized likelihood by normalizing the total likelihood of each state in respective HMM'"'"'s having the present time Gaussian distribution number, and finding a normalized likelihood by normalizing the total likelihood of each state in respective HMM'"'"'s having the immediately preceding Gaussian distribution number.

10. The acoustic model creating method according to claim 1

the plurality of HMM'"'"'s being syllable HMM'"'"'s corresponding to respective syllables.

11. The acoustic model creating method according to claim 10,

for plural syllable HMM'"'"'s having a same consonant or a same vowel among the syllable HMM'"'"'s, of state constituting the syllable HMM'"'"'s, initial states or plural states including the initial states in syllable HMM'"'"'s being tied for syllable HMM'"'"'s having the same consonant, and final states among states having self loops or plural states including the final states in syllable HMM'"'"'s being tied for syllable HMM'"'"'s having the same vowels.

14. A speech recognition apparatus to recognize an input speech, using HMM'"'"'s (Hidden Markov Models) as acoustic models with respect to feature data obtained through feature analysis on the input speech, the speech recognition apparatus comprising:

HMM'"'"'s created by the acoustic model creating method according to claim 1 are used as the HMM'"'"'s used as the acoustic models.

12. An acoustic model creating apparatus that optimizes Gaussian distribution numbers for respective states constituting an HMM (hidden Markov Model) for each state, and thereby creates an HMM having optimized Gaussian distribution numbers, the acoustic model creating apparatus comprising:

a distribution number setting device to increment a Gaussian distribution number step by step according to a specific increment rule for each state in plural HMM'"'"'s, and setting each state to a specific Gaussian distribution number;

a matching data creating device to create matching data by matching each state in respective HMM'"'"'s, which has been set to the specific Gaussian distribution number by the distribution number setting device, to training speech data;

a description length calculating device to find, according to a Minimum Description Length criterion, a description length of each state in respective HMM'"'"'s having a Gaussian distribution number at a present time to be outputted as a present time description length, and finding, according to the Minimum Description Length criterion, a description length of each state in respective HMM'"'"'s having a Gaussian distribution number immediately preceding the present time to be outputted as an immediately preceding description length, with the use of the matching data created by the matching data creating device; and

an optimum distribution number determining device to compare the present time description length with the immediately preceding description length in size, both of which are calculated by the description length calculating device, and to set an optimum Gaussian distribution number for each state in respective HMM'"'"'s on the basis of a comparison result.

13. An acoustic model creating program for use with a computer to optimize Gaussian distribution numbers for respective states constituting an HMM (hidden Markov Model) for each state, and thereby to create an HMM having optimized Gaussian distribution numbers, said acoustic model creating program comprising:

a distribution number setting procedural program for incrementing a Gaussian distribution number step by step according to a specific increment rule for each state in plural HMM'"'"'s, and setting each state to a specific Gaussian distribution number;

a matching data creating procedural program for creating matching data by matching each state in respective HMM'"'"'s, which has been set to the specific Gaussian distribution number in the distribution number setting procedure, to train speech data;

a description length calculating procedural program for finding, according to a Minimum Description Length criterion, a description length of each state in respective HMM'"'"'s having a Gaussian distribution number at a present time to be outputted as a present time description length, and finding, according to said Minimum Description Length criterion, a description length of each state in respective HMM'"'"'s having a Gaussian distribution number immediately preceding the present time to be outputted as an immediately preceding description length, with the use of the matching data created in said matching data creating procedural step; and

an optimum distribution number determining procedural program for comparing the present time description length with the immediately preceding description length in size, both of which are calculated in the description length calculating procedure, and setting an optimum Gaussian distribution number for each state in respective HMM'"'"'s on the basis of a comparison result.