JP2010169924A - Speech classification device, speech classification method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a speech classification device and a speech classification method capable of classifying speakers with a high degree of precision even for target speech data in which one speaker is a main speaker for a certain period of time, and a program for achieving these. <P>SOLUTION: The speech classification device 10 is used for performing clustering for a plurality of utterance segments contained in the speech data in which two or more speakers issue utterances, and classifying the speech based on the result of the clustering. The speech classification device 10 includes: a number of clusters counting part 2 for counting the number of clusters existing between two utterance segments when a plurality of utterance segments are arranged along a time axis; a distance calculation part 3 for calculating a distance based on the acoustic similarity between the two utterance segments using the counted number of clusters; and a clustering part 4 for performing the clustering of the plurality of the utterance segments using the calculated distance. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a voice classification device and a voice classification method, and more particularly to a voice classification device and a voice classification method for voice data when there is a bias in the utterance order or utterance frequency of speakers as in a conference or the like. Further, the present invention relates to a program for realizing these.

  Conventionally, utterances are divided based on silence sections and the like for conference audio data and the like, and are grouped for each similar utterance and classified for each speaker. Such a voice classification technique (speaker classification technique) is useful in the creation of conference minutes and the like and voice recognition.

  In addition, since labeling is performed for each class grouped for each utterance, and the same speaker label is assigned to the utterances by the same speaker in the speech data, the speech classification technology uses speaker labeling. It is also called technology. An example of a system using such a conventional speech classification technique (speaker labeling technique) is described in Non-Patent Document 1.

  Specifically, the system disclosed in Non-Patent Document 1 calculates a distance based on the acoustic similarity for each utterance included in input speech data, and performs clustering based on this distance. . At that time, the system disclosed in Non-Patent Document 1 not only uses acoustic similarity, but also introduces a heuristic that “a temporally continuous speech segment is likely to be uttered by the same speaker”. This improves the clustering accuracy of the utterance segment.

  That is, in the system disclosed in Non-Patent Document 1, when calculating the distance between two utterance segments, the distance calculated based on the acoustic similarity is set so that the target utterance segments are adjacent in time. Correct based on whether or not. For example, when the utterance segments are adjacent to each other, correction is performed so that the distance becomes smaller, and it is considered that the clustering accuracy is improved.

H. Jin, et al. “Automatic Speaker Clustering”, Proc. Of the 1997 DARPA Speech Recognition Workshop

  However, in the system disclosed in the above-mentioned non-patent document, for example, one speaker who is often seen at a conference or the like speaks as a questioner, and another speaker or speakers as answerers in the meantime. There is a problem that it does not work properly for voice data in the answer style. The reason is as follows.

  In other words, in the system disclosed in the above non-patent document, the distance calculation based on the acoustic similarity is performed considering only temporal adjacency relationships between the utterance segments, and the utterance segments are collectively obtained. The relationship between speaker clusters is not considered. For this reason, in the case of voice data in which the utterances of the same questioner are in between the utterances of different answerers, the utterance segments belonging to the same speaker are separated in time. Thus, it is difficult to construct an appropriate speaker cluster. As a result, the speaker labeling is not correctly given, and the system disclosed in the non-patent document becomes in a state of not operating properly.

  An object of the present invention is to solve the above-mentioned problem and to perform speech classification with high accuracy even when speech data whose main speaker is a single speaker is targeted for a certain period of time. An apparatus, a voice classification method, and a program for realizing the same are provided.

In order to achieve the above object, the speech classification apparatus according to the present invention performs clustering on a plurality of speech segments included in speech data in which two or more speakers are speaking, and based on the clustering result, the speech classification is performed. A voice classification device for performing
A cluster number measuring unit that measures the number of clusters existing between two utterance segments when the plurality of utterance segments are arranged along a time axis;
A distance calculation unit that calculates a distance based on acoustic similarity between the two utterance segments using the measured number of clusters;
And a clustering unit that performs clustering of the plurality of utterance segments using the calculated distance.

In order to achieve the above object, the speech classification method according to the present invention performs clustering on a plurality of speech segments included in speech data in which two or more speakers are speaking, and based on the clustering result, A speech classification method for performing speech classification,
(A) measuring the number of clusters existing between two utterance segments when the plurality of utterance segments are arranged along a time axis;
(B) calculating a distance based on an acoustic similarity between the two utterance segments using the number of clusters measured in the step (a);
(C) using the distance calculated in the step (b) to perform clustering of the plurality of utterance segments.

Furthermore, in order to achieve the above object, the program according to the present invention causes a computer to perform clustering on a plurality of speech segments included in speech data in which two or more speakers are speaking, and based on the clustering result. A program for performing classification,
In the computer,
(A) measuring the number of clusters existing between two utterance segments when the plurality of utterance segments are arranged along a time axis;
(B) calculating a distance based on an acoustic similarity between the two utterance segments using the number of clusters measured in the step (a);
(C) performing clustering of the plurality of utterance segments using the distance calculated in the step (b).

  With the above features, according to the speech classification apparatus, speech classification method, and program of the present invention, even when speech data whose main speaker is a single speaker is targeted for a certain period of time, the accuracy is high. Speaker classification can be performed.

FIG. 1 is a block diagram showing a schematic configuration of a speech classification apparatus according to an embodiment of the present invention. FIG. 2 is a diagram showing an example of audio data to be classified in the embodiment of the present invention. FIG. 3 is a diagram illustrating an example of a tree structure obtained by clustering by the speech classification apparatus illustrated in FIG. FIG. 4 is a flowchart showing processing performed by the speech classification method according to the embodiment of the present invention.

(Embodiment)
Hereinafter, a voice classification device, a voice classification method, and a program according to an embodiment of the present invention will be described with reference to FIGS. First, a schematic configuration of the speech classification apparatus according to the present embodiment will be described with reference to FIG. FIG. 1 is a block diagram showing a schematic configuration of a speech classification apparatus according to an embodiment of the present invention.

  The speech classification apparatus 10 according to the present embodiment shown in FIG. 1 targets speech data (see FIG. 2) in which two or more speakers are speaking, and performs clustering on a plurality of speech segments included in the speech data. This is a device for performing speech classification based on the clustering result.

  As shown in FIG. 1, the speech classification device 10 includes a cluster number measurement unit 2, a distance calculation unit 3, and a clustering unit 4. The cluster number measuring unit 2 measures the number of clusters existing between two utterance segments when a plurality of utterance segments are arranged along the time axis. The distance calculation unit 3 calculates the distance based on the acoustic similarity between the two utterance segments using the measured number of clusters. The clustering unit 4 performs clustering of a plurality of utterance segments using the calculated distance (see FIG. 3).

  Thus, unlike the system disclosed in the above non-patent document, the speech classification device 10 takes into account the relationship between clusters obtained by collecting utterance segments when calculating the distance based on the acoustic similarity. Therefore, even if speech segments belonging to the same speaker are separated in time, an appropriate distance can be given to them.

  As a result, the speech classifier 10 causes the cluster of the main speaker to appear alternately with the clusters of the other speakers when speech data in which one speaker is the main speaker is targeted for a certain period of time. The voice classification can be carried out in consideration of the property that it is often performed. As a result, according to the speech classification device 10, it is possible to classify speakers with high accuracy even when speech data in which a single speaker is an anchor person is targeted for a certain period of time.

  Here, the configuration and operation of the speech classification apparatus 10 according to the present embodiment will be described more specifically with reference to FIGS. FIG. 2 is a diagram showing an example of audio data to be classified in the embodiment of the present invention. FIG. 3 is a diagram illustrating an example of a tree structure obtained by clustering by the speech classification apparatus illustrated in FIG.

  The voice data shown in FIG. 2 is obtained by recording a conference in which a question is made by the speaker A and an answer is given by the speaker B or C. The voice data shown in FIG. 2 includes a plurality of utterance segments S1 to S11. Among these, the utterance segments S1, S2, S3, S6, S7, S10, and S11 are the utterance segments of the speaker A. The utterance segments S4 and S5 are the utterance segments of the speaker B, and the utterance segments S8 and S9 are the utterance segments of the speaker C.

As shown in FIG. 1, in the present embodiment, the speech classification device 10 further uses an input reception unit 1 that receives input of speech data from the outside and a clustering result by the clustering unit 4 to And a speaker classification unit 5 for performing classification (voice classification). The input receiving unit 1 has a function of extracting a speaker segment included in the voice data and outputting the extracted speaker segment to the cluster number measuring unit 2 in addition to a function as an interface for receiving external data input. I have. Specifically, for example, the input receiving unit 1 specifies voice data divided by a silent section, and extracts the voice data divided by the silent section as one utterance segment.

  Further, in the present embodiment, the measurement by the cluster number measurement unit 2, the calculation by the distance calculation unit 3, and the clustering by the clustering unit 4 are executed a plurality of times. Specifically, after the clustering unit 4 executes clustering, the cluster number measuring unit 2 newly measures the number of clusters existing between two utterance segments for the cluster formed thereby. Further, the distance calculation unit 3 newly calculates a distance based on the acoustic similarity between the two utterance segments using the newly measured number of clusters. Then, the clustering unit performs clustering again using the newly calculated distance. As will be described later with reference to FIG. 4, the calculation by the distance calculation unit 3 may be only correction after the second time.

  Further, the measurement by the cluster number measuring unit 2, the calculation by the distance calculating unit 3, and the clustering by the clustering unit 4 are performed until the tree structure of a plurality of segments shown in FIG. When the final clustering is completed, the speaker classification unit 5 cuts the tree structure shown in FIG. 3 at an appropriate position (for example, the position indicated by a broken line in FIG. 3), and based on the cluster state at that time Categorize the speakers. When the tree structure is cut along the broken line in FIG. 3, the speaker classification results coincide with the classification shown in FIG.

  The cluster number measuring unit 2 selects two utterance segments in order to measure the number of clusters. In the present embodiment, first, all combinations of all utterance segments included in the audio data are selected. Specifically, in the example of FIGS. 2 and 3, all selectable pairs are selected from the utterance segments S1 to S11.

  Here, a cluster that exists between two utterance segments refers to a cluster that exists between the end of the utterance segment with the earlier start time and the start of the other utterance segment. In the present embodiment, the cluster number measuring unit 2 can also measure the number of cluster time fragments and the number of types of clusters as the number of clusters.

  If clustering by the clustering unit 4 has not been executed yet, the cluster has not yet been formed. In this case, in the present embodiment, the cluster number measuring unit 2 regards each of the plurality of utterance segments as one cluster and measures the number of clusters existing between the two utterance segments.

  On the other hand, when clustering by the clustering unit 4 is executed once or more and a cluster including two or more utterance segments is formed, the cluster number measuring unit 2 measures the utterance segments determined to belong to the same cluster. It can also be excluded. Specifically, as shown in FIG. 3, for example, after the first clustering is completed, the number of clusters is not measured between the utterance segments S2 and S3.

  In addition, the number of clusters is measured between utterance segments and clusters, and also between clusters. In this case, the utterance at the start of the utterance segment with the earliest start time or the latest utterance at the end. The end of the segment is the end point or starting point of the measurement. Specifically, the cluster number measuring unit 2 measures the number of clusters existing between the end of the utterance segment S3 and the start of the utterance segment S6 when the utterance segments S1 to S3 become one cluster. At this time, as shown in FIGS. 2 and 3, the number of clusters is “1”.

  As described above, the distance calculation unit 3 uses the measured number of clusters to calculate a distance (hereinafter referred to as “distance”) based on the acoustic similarity between two utterance segments. In the present embodiment, the distance is calculated by obtaining the feature vectors of the two utterance segments, and using the obtained feature vectors. That is, the distance calculation unit 3 can calculate the distance by a Gaussian distribution obtained from the cepstrum, for example, as in the system disclosed in Non-Patent Document 1 described above.

  In this embodiment, the distance is calculated from the feature vector and then corrected according to the number of clusters. In the present embodiment, “calculating the distance using the number of clusters” means performing correction (such as multiplication of a correction coefficient) after calculating the distance, and may also perform only correction. means.

  Specifically, the distance can be calculated according to the following (Example 1) or (Example 2). The following (Example 1) and (Example 2) describe the case where the distance between the cluster A and the cluster B is calculated, but the same calculation is performed when the cluster element is only one utterance segment. it can.

In (Example 1) and (Example 2), the feature vector of an utterance segment belonging to cluster A is X _n (n = 1, 2, 3,... N ₁ ), and the feature vector of an utterance segment belonging to cluster B is _Let Y _n (n = 1, 2, 3,... N ₂ ). Here, N ₁ is the number of elements in cluster A, and N ₂ is the number of elements in cluster B.

(Example 1)
In Example 1, when the distance between the cluster A and the cluster B is d _cov , the distance d _cov can be obtained from the following ( _Expression 1) to ( _Expression 5). In the following (Equation 2), S ₁ and S ₂ represent sample covariance matrices of speech segments included in cluster A and cluster B.

(Example 2)
In Example 2, when the distance between the cluster A and the cluster B is d _MEAN , the distance d _MEAN can be obtained from the following ( _Expression 6) and (Expression 7). In the following (Equation 7), vector X ₁ and vector X ₂ represent the average vectors of speech segments included in cluster A and cluster B.

  Further, as described above, the distance calculation unit 3 corrects the calculated distance using the number of measured clusters in the calculation of the distance. For example, the distance calculation unit 3 can correct the distance so that the smaller the number of clusters existing between two utterance segments, the shorter the distance.

  Specifically, when the number of measured clusters is “0 (zero)” or “1”, correction is performed using the following (Equation 8), and the number of clusters is “0” and “1”. In the case other than “,” there is a mode in which no correction is performed. In the following (Equation 8), d represents the calculated distance before correction, and d ′ represents the distance after correction. Moreover, a is a positive number smaller than an arbitrary 1, and its size is set as appropriate. If no correction is performed, d = d ′.

  For example, in FIG. 3, when the utterance segments S1 to S3 become one cluster, the number of classes between the utterance segment S3 and the utterance segment S6 is “1”. In this case, the distance between the utterance segments S3 and S6 is corrected to be smaller than the calculated value.

  On the other hand, the distance calculation unit 3 can also correct the distance so that the distance becomes longer in direct proportion to the number of clusters. Specifically, a mode in which correction is performed using the following (Equation 9) can be given. In the following (Equation 9), d indicates the calculated distance before correction, and d ′ indicates the distance after correction. k indicates the number of measured clusters.

  In the present embodiment, the distance calculation unit 3 uses the number of clusters between two utterance segments until the number of clusters falls below a certain value because the number of clusters is large at the initial stage of the clustering process. The correction may not be performed.

  Specifically, the distance calculation unit 3 compares the total number of a plurality of utterance segments with a threshold value when clustering by the clustering unit 4 is not executed when calculating the distance. Further, when the clustering unit executes clustering when calculating the distance, the distance calculation unit 3 compares the total number of clusters formed thereby with a threshold value. Then, as a result of the comparison, the distance calculation unit 3 may use the number of clusters for the calculation of the distance (correction based on the number of clusters) only when the total number of the plurality of utterance segments or the total number of clusters is smaller than the threshold value. it can.

  Furthermore, in the present embodiment, the distance calculation unit 3 can also perform correction using the adjacency relationship disclosed in Non-Patent Document 1 described above, in addition to correction based on the number of measured clusters. In this case, further improvement in speaker classification accuracy can be expected. In addition, the distance calculation unit 3 basically calculates the distance for all combinations of segments. For example, the speaker segments that have been identified as having the same speaker in advance. For the interval, the calculation of the distance can be omitted from the viewpoint of improving the processing speed.

  As described above, the clustering unit 4 performs clustering according to the distance between utterance segments obtained by the distance calculation unit 3. In the present embodiment, the clustering process performed by the clustering unit 4 is not particularly limited, and may be a clustering process using the method disclosed in Non-Patent Document 1 described above.

Specifically, the clustering unit 4 first, for a focused utterance segment, and two or more utterance segments (or a cluster to which the focused utterance segment does not belong)
Compare the distance with. Then, the clustering unit 4 identifies an utterance segment having the shortest distance from the focused utterance segment, and forms a cluster with the focused utterance segment. When the focused utterance segment is already included in the cluster, the clustering unit 4 also includes the utterance segment with the shortest distance in the cluster.

  On the contrary, when the utterance segment with the shortest distance is already included in the cluster, the clustering 4 assumes that the focused utterance segment is included in this cluster. Further, when the focused utterance segment and the utterance segment with the shortest distance are both included in different clusters, the clustering 4 unifies both the clusters into one.

  In the present embodiment, the comparison of distances by the clustering unit 4 can be performed using, for example, a conventionally known nearest neighbor method, farthest neighbor method, group average method, or the like. The “distance between the focused utterance segment and the cluster” in the above means the distance between the focused utterance segment and the utterance segment included in the cluster.

  When the clustering by the clustering 4 is repeatedly performed, a tree structure is formed by the utterance segments S1 to S11 as shown in FIG. Thereafter, the speaker classifying unit 5 cuts the tree structure at an appropriate position and classifies the speakers. This cutting position can be obtained, for example, in advance by an experiment using test data that is clear to the speaker of each utterance segment. As a result of the classification by the speaker classification unit 5, when the two utterance segments are viewed in time order, it becomes clear how many speakers existed between them.

  Next, the speech classification method according to the embodiment of the present invention will be described with reference to FIG. FIG. 4 is a flowchart showing processing performed by the speech classification method according to the embodiment of the present invention. The speech classification method according to the present embodiment is implemented by operating the speech classification apparatus according to the present embodiment shown in FIG. For this reason, the description of the speech classification method in the present embodiment will be made while explaining the operation of the speech classification device 10 with appropriate reference to FIGS.

  First, as shown in FIG. 4, as an initialization process, the distance calculation unit 3 first receives a combination of speech segments (see FIG. 2), and targets all combinations (pairs) of utterance segments. A distance based on the acoustic similarity between the two utterance segments is calculated (step A1).

  Note that step A1 may be executed after step A2 described below. However, in this case, when the second and subsequent steps A2 are executed, step A1 is skipped. Further, in step A1, it is possible to omit the calculation of the distance between speaker segments in which the same speaker is specified in advance from the viewpoint of improving the processing speed.

  Next, the cluster number measuring unit 2 measures the number of clusters existing between two utterance segments (step A2). When clustering has not been executed yet, the cluster has not yet been formed. In this case, in the present embodiment, the cluster number measuring unit 2 regards each of the plurality of utterance segments as one cluster and measures the number of clusters existing between the two utterance segments.

  Next, the distance calculation unit 3 corrects the distance calculated in step A1 using the number of clusters measured in step A2, and calculates the distance by correction (step A3). Thereby, the value of the distance between segments becomes a value in which the relationship between clusters is considered.

Next, the clustering unit 4 identifies an utterance segment (or cluster) having the smallest distance from each utterance segment of interest (step A4), and forms a cluster (step A5). The cluster is formed in step A5 by, for example, forming a cluster with the focused utterance segment and the utterance segment with the smallest distance, or including the utterance segment with the smallest distance in the cluster including the focused utterance segment. Etc.

  Next, after the end of step A5, the clustering unit 4 determines whether or not the tree structure (see FIG. 3) is completed by clustering processing and the number of clusters becomes one (step A6). As a result of the determination, if the number of clusters is not yet one, steps A2 to A5 are executed again. On the other hand, if there is only one cluster, the speaker classification unit 5 cuts the tree structure at an appropriate position and classifies the speakers (step A7). Thereafter, all processing is completed.

  As described above, in the speech classification method according to the present embodiment, when calculating the distance based on the acoustic similarity, the relationship between clusters obtained by collecting the speech segments is considered. Therefore, even if utterance segments belonging to the same speaker are separated in time, an appropriate distance is given to them, and even if voice data for which one speaker is an anchor person is targeted for a certain period of time Therefore, speaker classification with high accuracy is possible.

  Moreover, the program in this Embodiment should just be a program which makes a computer perform step A1-A7 shown in FIG. And if the program in this Embodiment is installed in a computer and this is run, the speech classification apparatus 10 and the speech classification method in this Embodiment can be embodied. In this case, a central processing unit (CPU) of the computer functions as an input receiving unit 1, a cluster number measuring unit 2, a distance calculating unit 3, a clustering unit 4, and a speaker classifying unit 5 to perform processing.

  As described above, according to the present invention, it is possible to provide speaker classification with high accuracy. Therefore, the present invention is effective for a conference record creation device and a speech recognition device for supporting speaker labeling, and has industrial applicability.

DESCRIPTION OF SYMBOLS 1 Input reception part 2 Cluster number measurement part 3 Distance calculation part 4 Clustering part 5 Speaker classification part 10 Voice classification apparatus

Claims (18)

A speech classification device that performs clustering on a plurality of speech segments included in speech data in which two or more speakers are speaking, and performs speech classification based on the clustering result,
A cluster number measuring unit that measures the number of clusters existing between two utterance segments when the plurality of utterance segments are arranged along a time axis;
A distance calculation unit that calculates a distance based on acoustic similarity between the two utterance segments using the measured number of clusters;
A speech classification apparatus comprising: a clustering unit that performs clustering of the plurality of utterance segments using the calculated distance. The cluster number measuring unit newly measures the number of clusters existing between the two utterance segments for the clusters formed by the clustering unit after the clustering is executed,
The distance calculation unit newly calculates the distance based on the acoustic similarity between the two utterance segments using the newly measured number of clusters,
The speech classification apparatus according to claim 1, wherein the clustering unit performs the clustering again using the newly calculated distance. When the cluster number measuring unit is not clustered by the clustering unit, each of the plurality of utterance segments is regarded as one cluster, and the number of clusters existing between the two utterance segments is measured,
The clustering by the clustering unit performs the measurement by the cluster number measuring unit, the calculation by the distance calculating unit, and the clustering by the clustering unit a plurality of times until the tree structure of the plurality of segments is completed. The speech classification apparatus according to 2.   The speech classification apparatus according to claim 1, wherein the distance calculation unit corrects the distance so that the distance decreases as the number of clusters existing between the two utterance segments decreases. . When the distance calculation unit calculates the distance,
When clustering by the clustering unit is not executed, the total number of the plurality of utterance segments is compared with a threshold value, and when clustering by the clustering unit is executed, the total number of clusters formed thereby and the Contrast with threshold,
The speech classification according to any one of claims 1 to 4, wherein the number of clusters is used to calculate the distance only when the total number of the plurality of utterance segments or the total number of the clusters is smaller than the threshold as a result of comparison. apparatus.   The distance calculation unit calculates a feature vector of each of the two utterance segments, and calculates a distance based on an acoustic similarity between the two utterance segments using each of the calculated feature vectors. The speech classification device according to any one of? A speech classification method for performing clustering on a plurality of speech segments included in speech data in which two or more speakers are speaking, and performing speech classification based on the clustering result,
(A) measuring the number of clusters existing between two utterance segments when the plurality of utterance segments are arranged along a time axis;
(B) calculating a distance based on an acoustic similarity between the two utterance segments using the number of clusters measured in the step (a);
(C) using the distance calculated in the step (b) to perform clustering of the plurality of utterance segments. After executing the clustering in the step (c), the step (a) is newly executed on the cluster formed by the clustering, and in the new step (a), the two utterance segments are Measure the number of clusters in between,
After the new step (a) is executed, the step (b) is newly executed, and the acoustic similarity between the two utterance segments is calculated using the newly measured number of clusters. A new distance based on
The speech classification method according to claim 7, wherein after the new step (b) is performed, the clustering in the step (c) is performed again using the newly calculated distance. When clustering by the step (c) is not executed, the number of clusters existing between the two utterance segments in the step (a), considering each of the plurality of utterance segments as one cluster. Measure
Until the tree structure of the plurality of segments is completed by the clustering in the step (c), the measurement in the step (a), the calculation in the step (b), and the clustering in the step (c) The voice classification method according to claim 8, which is executed a plurality of times.   The voice according to any one of claims 7 to 9, wherein in the step (b), the distance is corrected such that the smaller the number of clusters existing between the two utterance segments, the shorter the distance. Classification method. When calculating the distance in the step (b),
When the clustering by the step (c) is not executed, the total number of the plurality of utterance segments is compared with the threshold value, and when the clustering by the step (c) is executed, it is formed thereby. Contrast the total number of clusters and the threshold value,
The speech classification according to any one of claims 7 to 10, wherein the number of clusters is used for calculating the distance only when the total number of the plurality of utterance segments or the total number of the clusters is smaller than the threshold as a result of the comparison. Method.   In the step (b), a feature vector of each of the two utterance segments is obtained, and a distance based on an acoustic similarity between the two utterance segments is calculated using each of the obtained feature vectors. Item 12. The speech classification method according to any one of Items 7 to 11. A program for causing a computer to perform clustering on a plurality of utterance segments included in speech data in which two or more speakers are speaking, and to perform speech classification based on the clustering result,
In the computer,
(A) measuring the number of clusters existing between two utterance segments when the plurality of utterance segments are arranged along a time axis;
(B) calculating a distance based on an acoustic similarity between the two utterance segments using the number of clusters measured in the step (a);
(C) executing the clustering of the plurality of utterance segments using the distance calculated in the step (b). After executing the clustering in the step (c), the step (a) is newly executed on the cluster formed by the clustering, and in the new step (a), the two utterance segments are Measure the number of clusters in between,
After the new step (a) is executed, the step (b) is newly executed, and the acoustic similarity between the two utterance segments is calculated using the newly measured number of clusters. A new distance based on
The program according to claim 13, wherein after the new step (b) is executed, the clustering in the step (c) is executed again using the newly calculated distance. When clustering by the step (c) is not executed, the number of clusters existing between the two utterance segments in the step (a), considering each of the plurality of utterance segments as one cluster. Measure
Until the tree structure of the plurality of segments is completed by the clustering in the step (c), the measurement in the step (a), the calculation in the step (b), and the clustering in the step (c) The program according to claim 14, which is executed a plurality of times.   The program according to any one of claims 13 to 15, wherein in the step (b), the distance is corrected such that the smaller the number of clusters existing between the two utterance segments, the shorter the distance. . When calculating the distance in the step (b),
When the clustering by the step (c) is not executed, the total number of the plurality of utterance segments is compared with the threshold value, and when the clustering by the step (c) is executed, it is formed thereby. Contrast the total number of clusters and the threshold value,
The program according to any one of claims 13 to 16, wherein the number of clusters is used for calculating the distance only when the total number of the plurality of utterance segments or the total number of the clusters is smaller than the threshold as a result of the comparison.   In the step (b), a feature vector of each of the two utterance segments is obtained, and a distance based on an acoustic similarity between the two utterance segments is calculated using each of the obtained feature vectors. Item 18. The program according to any one of Items 13 to 17.