SEGMENTATION USING PRIOR DISTRIBUTIONS

1 . A computer-implemented method comprising:

obtaining a speech signal;

estimating, by one or more processing devices, a first set of segment boundaries using the speech signal, wherein the first set of segment boundaries is determined using a first segmentation process;

estimating, by the one or more processing devices, a second set of segment boundaries using the speech signal, wherein the second set of segment boundaries is determined using a second segmentation process that is different from the first segmentation process;

obtaining a model corresponding to a distribution of segment boundaries;

computing a first score indicative of a degree of similarity between the model and the first set of segment boundaries;

computing a second score indicating a degree of similarity between the model and the second set of segment boundaries;

selecting a set of segment boundaries using the first score and the second score; and

processing the speech signal using the selected set of segment boundaries.

2 . The method of claim 1 , wherein computing the first score comprises:

computing, by the one or more processing devices, a first distribution function associated with the first set of boundaries, wherein the first distribution function is representative of an attribute associated with speech segments within the speech signal; and

computing, by the one or more processing devices, the first score based on a degree of statistical similarity between (i) the first distribution function and (ii) the model, the model being representative of the attribute associated with speech segments identified from speech signals in a training corpus.

3 . The method of claim 2 , wherein computing the second score comprises:

computing, by the one or more processing devices, a second distribution function associated with the second set of boundaries, wherein the second distribution function is also representative of the attribute; and

computing, by the one or more processing devices, the second score based on a degree of statistical similarity between (i) the second distribution function and (ii) the model.

4 . The method of claim 1 , wherein selecting the set of segment boundaries using the first score and the second score comprises:

determining that the first score is higher than the second score or the second score is higher than the first score;

responsive to determining that the first score is higher than the second score, selecting the first set of segment boundaries as the set of segment boundaries; and

responsive to determining that the second score is higher than the first score, selecting the second set of segment boundaries as the set of segment boundaries.

5 . The method of claim 1 , wherein estimating the first set of segment boundaries comprises:

obtaining a plurality of frequency representations by computing a frequency representation of each of multiple portions of the speech signal;

generating, by one or more processing devices, a time-varying data set using the plurality of frequency representations by computing a representative value of each frequency representation of the plurality of frequency representations; and

determining, by the one or more processing devices, the first set of segment boundaries using the time-varying data set.

6 . The method of claim 5 , wherein the representative value of each frequency representation is a stripe function value associated with the frequency representation.

7 . The method of claim 5 , wherein computing the frequency representation comprises computing a stationary spectrum.

8 . The method of claim 5 , wherein the representative value of each frequency representation is an entropy of the frequency representation.

9 . The method of claim 1 , wherein the first segmentation process is different from the second segmentation process with respect to a parameter associated with each of the segmentation processes.

10 . The method of claim 2 , wherein the attribute comprises one of: a duration of speech segments, a width of time-gap between consecutive speech segments, a number of speech segments within an utterance, a number of speech segments per unit time, or a duration between starting points of consecutive speech segments.

11 . The method of claim 3 , wherein each of the first distribution function and the second distribution function is a cumulative distribution function (CDF).

12 . The method of claim 3 , wherein each of the first distribution function and the second distribution function is a probability density function (PDF).

13 . The method of claim 3 , wherein each of the first score and the second score is indicative of a goodness-of-fit between the model and the corresponding one of the first and second distribution function.

14 . The method of claim 13 , wherein the goodness-of-fit is computed based on a Kolmogorov-Smirnov test between the model and the corresponding one of the first and second distribution functions.

15 . The method of claim 1 , wherein processing the speech signal comprises performing one of: speech recognition or speaker identification.

16 . A system comprising:

memory; and

one or more processing devices configured to:

obtain a speech signal,

estimate a first set of segment boundaries using the speech signal, wherein the first set of segment boundaries is determined using a first segmentation process,

estimate a second set of segment boundaries using the speech signal, wherein the second set of segment boundaries is determined using a second segmentation process that is different from the first segmentation process,

obtain a model corresponding to a distribution of segment boundaries,

compute a first score indicative of a degree of similarity between the model and the first set of segment boundaries,

compute a second score indicating a degree of similarity between the model and the second set of segment boundaries,

select a set of segment boundaries using the first score and the second score, and

process the speech signal using the selected set of segment boundaries.

17 . The system of claim 16 , wherein wherein the one or more processing devices are configured to:

compute a first distribution function associated with the first set of boundaries, wherein the first distribution function is representative of an attribute associated with speech segments within the speech signal; and

compute the first score based on a degree of statistical similarity between (i) the first distribution function and (ii) the model, the model being representative of the attribute associated with speech segments identified from speech signals in a training corpus.

18 . The system of claim 17 , wherein the one or more processing devices are further configured to:

compute a second distribution function associated with the second set of boundaries, wherein the second distribution function is also representative of the attribute; and

compute the second score based on a degree of statistical similarity between (i) the second distribution function and (ii) the model.

19 . The system of claim 16 , wherein selecting the set of segment boundaries using the first score and the second score comprises:

determining that the first score is higher than the second score or the second score is higher than the first score;

responsive to determining that the first score is higher than the second score, selecting the first set of segment boundaries as the set of segment boundaries; and

responsive to determining that the second score is higher than the first score, selecting the second set of segment boundaries as the set of segment boundaries.

20 . The system of claim 16 , wherein estimating the first set of segment boundaries comprises:

obtaining a plurality of frequency representations by computing a frequency representation of each of multiple portions of the speech signal;

generating a time-varying data set using the plurality of frequency representations by computing a representative value of each frequency representation of the plurality of frequency representations; and

determining the first set of segment boundaries using the time-varying data set.

21 . The system of claim 20 , wherein the representative value of each frequency representation is one of a stripe function value or entropy value associated with the frequency representation.

22 . The system of claim 20 , wherein the frequency representation is computed by computing a stationary spectrum.

23 . The system of claim 16 , wherein the first segmentation process is different from the second segmentation process with respect to a parameter associated with each of the segmentation processes.

24 . The system of claim 17 , wherein the attribute comprises one of: a duration of speech segments, a width of time-gap between consecutive speech segments, a number of speech segments within an utterance, a number of speech segments per unit time, or a duration between starting points of consecutive speech segments.

25 . The system of claim 18 , wherein each of the first score and the second score is indicative of a goodness-of-fit between the model and the corresponding one of the first and second distribution function.

26 . The system of claim 16 , further comprising a speech recognition engine to perform speech recognition or a speaker identification engine to perform speaker identification.

27 . One or more machine-readable storage devices having encoded thereon computer readable instructions for causing one or more processors to perform operations comprising:

obtaining a speech signal;

estimating a first set of segment boundaries using the speech signal, wherein the first set of segment boundaries is determined using a first segmentation process;

estimating a second set of segment boundaries using the speech signal, wherein the second set of segment boundaries is determined using a second segmentation process that is different from the first segmentation process;

obtaining a model corresponding to segment boundaries;

computing a first score indicative of a degree of similarity between the model and the first set of segment boundaries;

computing a second score indicating a degree of similarity between the model and the second set of segment boundaries;

selecting a set of segment boundaries using the first score and the second score; and

processing the speech signal using the selected set of segment boundaries.

28 . The one or more machine-readable storage devices of claim 27 , wherein computing the first score comprises:

computing a first distribution function associated with the first set of boundaries, wherein the first distribution function is representative of an attribute associated with speech segments within the speech signal; and

computing the first score based on a degree of statistical similarity between (i) the first distribution function and (ii) the model, the model being representative of the attribute associated with speech segments identified from speech signals in a training corpus.

29 . The one or more machine-readable storage devices of claim 28 , wherein computing the second score comprises:

computing a second distribution function associated with the second set of boundaries, wherein the second distribution function is also representative of the attribute; and

computing the second score based on a degree of statistical similarity between (i) the second distribution function and (ii) the model.

30 . The one or more machine-readable storage devices of claim 27 , wherein estimating the first set of segment boundaries comprises:

obtaining a plurality of frequency representations by computing a frequency representation of each of multiple portions of the speech signal;

generating a time-varying data set using the plurality of frequency representations by computing a representative value of each frequency representation of the plurality of frequency representations; and

determining the first set of segment boundaries using the time-varying data set.