JP7010905B2 - Information processing equipment, information processing methods and programs - Google Patents

Description

The present invention relates to an information processing apparatus, an information processing method and a program.

Speech recognition has been conventionally known as a technique for causing a computer to recognize a human voice or the like. In speech recognition, input speech is recognized using three models called an acoustic model, a recognition dictionary, and a language model (see, for example, Non-Patent Document 1). The acoustic model is a model in which phonemes and voice characteristics are associated with each other, and the acoustic characteristics of phonemes are structured and expressed as probability values.

The performance of an acoustic model depends on the quality and quantity of audio data used for its learning. Therefore, in order to learn the acoustic model, it is necessary to prepare a large amount of voice data of sufficient quality (for example, prepare a quantity of voice data having a total playback time exceeding 1000 hours).

Hirokazu Masataki, Daisuke Shibata, Yuichi Nakazawa, Satoshi Kobashikawa, Atsunori Ogawa, Katsutoshi Otsuki, "Free speech recognition technology" VoiceRex "to listen to natural conversations with customers", NTT Technology Journal 2006 Vol.18 No.11

However, since the voice data may include personal information, confidential information, and the like, it may be difficult to obtain a large amount of voice data. For example, if the service provider who provides the service using voice recognition and the owner of the voice data are different persons, the service provider may include personal information, confidential information, and the like. It is generally difficult to receive an offer from the owner.

For this reason, for example, an acoustic model can be created with a relatively small amount of voice data (for example, voice data having a total playback time of several hours to several tens of hours) that is confirmed not to contain personal information or confidential information. There were cases where the performance of the acoustic model was not sufficient due to learning.

On the other hand, it is known that even if the playback time of each audio data is very short (for example, several hundred milliseconds or less), the acoustic model can acquire sufficient performance if there is a large amount of audio data. Has been done. Therefore, in order to learn the acoustic model, it is not always necessary to have audio data having a reproduction time (for example, 1 second to several seconds or more) in which personal information, confidential information, and the like can be heard.

The embodiment of the present invention has been made in view of the above points, and an object of the present invention is to create subdivided data as learning data of an acoustic model so that the information represented by the original voice data cannot be restored.

In order to achieve the above object, the information processing apparatus according to the present embodiment is predetermined from the extraction means for extracting the time-series data of the feature amount from the input voice data and the time-series data extracted by the extraction means. The partial time-series creation means that creates a plurality of partial time-series data by cutting out the feature amount for each time width of the above and the plurality of partial time-series data created by the partial time-series creation means are randomly connected. It is characterized by having a training data creating means for creating training data used for learning an acoustic model.

As the learning data of the acoustic model, it is possible to create subdivided data so that the information represented by the original voice data cannot be restored.

It is a figure which shows an example of the whole structure of this embodiment. It is a flowchart which shows an example of the creation of a training data and the learning of an acoustic model. It is a figure (the 1) for schematically explaining the flow of learning data creation. It is a figure (2) for schematically explaining the flow of learning data creation.

Hereinafter, an embodiment of the present invention (hereinafter, also referred to as “the present embodiment”) will be described. In the present embodiment, the information expressed by the voice is subdivided so that the information represented by the voice data cannot be restored by using the voice data having a audible reproduction time (for example, 1 second to several seconds or more). The case of creating the training data of the acoustic model will be described.

Here, examples of the voice data assumed by the present embodiment include voice data representing a call record of a call center. This is because such voice data often includes customer's personal information (for example, name, address, etc.). Therefore, in the present embodiment, it is assumed that the call record of the call center is used as the voice data, and the voice data includes personal information. However, this is only an example, and this embodiment can be applied to arbitrary voice data. An example of voice data containing confidential information is voice data representing a meeting record.

<Overall configuration>
First, the overall configuration of this embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of the overall configuration of the present embodiment.

As shown in FIG. 1, in the present embodiment, a learning data creating device 10 for creating learning data of an acoustic model and an acoustic model learning device 20 for learning (tuning) an acoustic model using the learning data are used. Be done. The learning data creation device 10 and the acoustic model learning device 20 are realized by, for example, a computer (information processing device) such as a PC (personal computer) or a general-purpose server, or a computer system (information processing system). The learning data creating device 10 and the acoustic model learning device 20 may be realized by, for example, a smartphone, a tablet terminal, or the like.

The learning data creating device 10 takes the voice data 1000 as an input and subdivides the information represented by the voice data 1000 into non-audible times to create the learning data 2000. It is assumed that the voice data 1000 includes personal information.

Here, the learning data creating device 10 has a feature amount extracting unit 101, a partial time series creating unit 102, a noise adding unit 103, an order rearranging unit 104, and a learning data creating unit 105. Each of these functional units is realized by a process of causing a processor or the like to execute one or more programs stored in a memory device (for example, an auxiliary storage device or the like) of the learning data creation device 10. Note that one or more of these programs may be stored in, for example, an external recording medium (for example, a CD, DVD, USB memory, etc.) connected to the learning data creation device 10.

The feature amount extraction unit 101 extracts a feature amount representing an acoustic feature from the audio data 1000, and creates time-series data of the feature amount (hereinafter, also referred to as “feature amount time series”). This feature quantity time series can be obtained by applying a known method (for example, cepstrum analysis or the like) to the voice data 1000. Since it is possible to restore the original voice data 1000 by inversely converting the feature quantity time series, it can be said that the feature quantity time series contains personal information.

The feature amount extraction unit 101 may input the voice data 1000 stored in the memory device, may input the voice data 1000 stored in the external recording medium, or may input the voice data 1000 stored in the external recording medium, via a communication network. The voice data 1000 stored in the server device or the like connected to the device may be downloaded and input.

The partial time series creation unit 102 cuts out the feature amount time series created by the feature amount extraction unit 101 in units of a predetermined cutout time width (hereinafter, also referred to as “frame length”) (hereinafter, “partial”). Also referred to as "time series"). At this time, the partial time series creation unit 102 partially shifts (slides) the cutting start time by a predetermined time width shorter than the frame length (hereinafter, also referred to as “frame shift amount”) from the feature amount time series. Create a time series. Note that each partial time series is given a correct answer label (that is, information representing the correct phoneme) used when learning the acoustic model.

The noise addition unit 103 adds noise to each of the plurality of partial time series created by the partial time series creation unit 102. This is because, as will be described later, it is possible to restore the original feature time series from multiple partial time series, so it is not possible to restore the original feature time series by adding noise. To do.

The order rearranging unit 104 randomly rearranges the order of a plurality of partial time series to which noise is added by the noise adding unit 103. This is to make it more difficult to restore the original feature quantity time series.

The learning data creation unit 105 creates learning data in which the partial time series sorted by the order sorting unit 104 are combined. This learning data may be stored in a memory device or the like of the learning data creation device 10, or may be passed to the acoustic model learning device 20. When the learning data is passed to the acoustic model learning device 20, the learning data may be transmitted via the communication network, or the learning data is passed via an external recording medium or the like in which the learning data is stored. You may.

The acoustic model learning device 20 takes the learning data 2000 as an input and learns (tunes) the acoustic model 3000 using the learning data 2000.

Here, the acoustic model learning device 20 has an acoustic model learning unit 201. The acoustic model learning unit 201 is realized by a process of causing a processor or the like to execute one or more programs stored in a memory device (for example, an auxiliary storage device or the like) of the acoustic model learning device 20. It should be noted that one or more of these programs may be stored in, for example, an external recording medium (for example, a CD, a DVD, a USB memory, etc.) connected to the acoustic model learning device 20.

The acoustic model learning unit 201 learns (tunes) the acoustic model 3000 stored in the memory device or the like of the acoustic model learning apparatus 20 by using the learning data passed from the learning data creating apparatus 10. At this time, the acoustic model learning unit 201 cuts out the learning data for each time width having the same time width as the above-mentioned frame length, and then these cut out data (that is, the partial time series to which noise is added and its correct answer label). The acoustic model 3000 is learned using. As the acoustic model 3000, an arbitrary model in which a phonetic element and a feature amount are associated with each other can be used, and for example, a hidden Markov model (HMM) or the like can be used. Further, as a learning algorithm for learning the acoustic model 3000, an arbitrary algorithm can be used depending on the model, and for example, an EM (Expectation-Maximization) algorithm or the like can be used.

In the example shown in FIG. 1, the case where the learning data creating device 10 and the acoustic model learning device 20 are different devices has been described, but the present invention is not limited to this, and the learning data creating device 10 and the acoustic model learning device 20 are used. It may be integrally configured.

<Processing flow>
Next, processing in the case where the learning data 2000 is created by the learning data creating device 10 according to the present embodiment and then the acoustic model 3000 is learned using the learning data 2000 by the acoustic model learning device 20 according to the present embodiment. The flow of the above will be described with reference to FIG. FIG. 2 is a flowchart showing an example of creating learning data and learning an acoustic model.

First, the feature amount extraction unit 101 of the learning data creation device 10 extracts the feature amount from the input voice data 1000 and creates a feature amount time series (step S101). For example, as shown in FIG. 3, when the voice data 1000 representing the voice "Thank you for the phone" is input, the feature amount extraction unit 101 extracts the feature amount from the voice data 1000 and the feature amount time series. Create 1100. As described above, the feature amount extraction unit 101 can extract the feature amount from the voice data 1000 by a known method such as cepstrum analysis and create a feature amount time series.

Next, the partial time series creation unit 102 of the learning data creation device 10 cuts out the feature amount of the frame length from the feature amount time series 1100 while shifting the cutout start time by the frame shift amount, thereby performing a plurality of partial time series. Is created (step S102). For example, assuming that both the frame shift amount and the frame length are fixed and are represented as Δ and T, respectively, as shown in FIG. 3, the partial time series creation unit 102 frame shifts from the beginning of the feature quantity time series 1100. While shifting the cutting start time by the amount Δ, the feature amount of the time width of the frame length T is cut out as a partial time series 1200. The frame length T and the frame shift amount Δ can be arbitrarily set on the condition that T> Δ is satisfied. For example, the frame length T is about several hundred milliseconds and the frame shift amount Δ is about several hundred milliseconds. It may be several tens of milliseconds.

Specifically, first, the partial time series creation unit 102 cuts out the feature amount of the time width from the cutout start time t = 0 to the cutout end time t = T from the feature amount time series 1100 as the partial time series 1200-1. .. Next, the partial time series creation unit 102 cuts out the feature amount having a time width from the cutout start time t = Δ to the cutout end time t = T + Δ as the partial time series 1200-2 and cuts out from the feature amount time series 1100. Similarly, when the nth cutout is performed thereafter, the partial time series creation unit 102 determines the time from the cutout start time t = (n-1) Δ to the cutout end time t = T + (n-1) Δ. The feature amount of the width is set as a partial time series 1200-n and cut out from the feature amount time series 1100.

As a result, a plurality of partial time series 1200 can be obtained. In the following, when the total number of partial time series 1200 is N and each partial time series 1200 is distinguished, "partial time series 1200-1", "partial time series 1200-2", ..., "partial time series" Also referred to as "1200-N".

Here, each partial time series 1200 is given a correct answer label used when learning the acoustic model 3000. In the example shown in FIG. 3, the correct answer label "/ o /" is given to the partial time series 1200-1, and the correct answer label "/ d /" is given to the partial time series 1200-2. ing. Further, the correct answer label "/ e /" is given to the partial time series 1200-3, and the correct answer label "/ n /" is given to the partial time series 1200-4. These correct answer labels may be given to each partial time series 1200 by any method. For example, the correct answer labels may be given manually by the user, or the voice data is labeled in phoneme units. A correct label may be given by a tool (for example, a phoneme segmentation tool) or the like.

Next, the noise addition unit 103 of the learning data creation device 10 adds noise to each of the plurality of partial time series 1200 (step S103).

As described above, since the frame length T and the frame shift amount Δ satisfy T> Δ, the partial time series 1200-n cut out at the nth time and the partial time series 1200- (n + 1) cut out at the n + 1th time are different. At least a part of the time interval is superimposed, and the feature quantity changes in the same time within this time interval. Therefore, the feature quantity time series 1100 is obtained by searching for the partial time series 1200 including the time interval in which the feature quantity changes in the same manner among the plurality of partial time series 1200 and combining them with each other so that the time intervals overlap with each other. (That is, it is possible to restore the audio data 1000 from the partial time series 1200). Therefore, by adding different noise to each of the partial time series 1200, the feature quantity time series 1100 cannot be restored from the partial time series 1200.

The noise addition unit 103 may add different noise to each of the partial time series 1200 by any method. For example, when n = 1, ..., N, the noise addition unit 103 may add different noise to the partial time series 1200-n. After generating the random number rn, it is conceivable to add (or subtract, etc.) the random number _rn to each feature quantity included in the partial time series 1200- _n . However, each random number rn has different feature quantities in the time interval superimposed between the partial time series 1200- _n and the partial time series 1200- (n + 1), and the learning of the acoustic model 3000 is not affected. It is preferable to set it to a slight value of. In the following, the partial time series 1200 to which noise is added is also referred to as "partial time series 1200".

Next, the order rearranging unit 104 of the learning data creation device 10 randomly rearranges the order of each partial time series 1200 (step S104). That is, for example, as shown in FIG. 4, the order rearranging unit 104 has a partial time series 1200-1, a partial time series 1200-2, ..., A partial time series 1200-N, and a partial time series 1200-n. ₁ , Partial time series 1200-n ₂ , ..., Sort to partial time series 1200-n _N. Here, n ₁ ∈ {1, ..., N}, n ₂ ∈ {1, ..., N} \ {n ₁ }, n ₃ ∈ {1, ..., N} \ {n ₁ , N ₂ }, ..., n _N ∈ {1, ..., N} \ {n ₁ , ..., n _N-1 }. In the example shown in FIG. 4, n ₁ = 3, n ₂ = 4, n ₃ = 1, and n ₄ = 2.

The order rearranging unit 104 may randomly rearrange the order of each partial time series 1200 by any method. For example, when n = 1, ..., N, a random number is given to the partial time series 1200-n. After generating s _n , it is conceivable to rearrange the partial time series 1200-n in ascending order (or descending order) of the random numbers s _n . At this time, a random number sn is generated from a range sufficiently exceeding the total number N of the partial time series 1200 (for example, S is an integer sufficiently larger than _N , and a random number _sn is generated from [0, S], or [-. It is preferable to generate a random number _sn from S, S]). Further, it is preferable not to generate the same random number for different partial time series 1200.

Based on the above, the partial time series 1200-1, the partial time series 1200-2, ..., The partial time series 1200-n ₁ in which the partial time series 1200-N are randomly rearranged, the partial time series 1200-n ₂ , ... ..., Partial time series 1200-n _N is obtained. This makes it more difficult to restore the original feature quantity time series 1100.

Next, the learning data creation unit 105 of the learning data creation device 10 combines the rearranged partial time series 1200 to create the learning data 2000 (step S105). That is, for example, as shown in FIG. 4, the learning data creation unit 105 combines the partial time series 1200-n _m and the partial time series 1200-n _{m + 1} with each other with m = 1, ..., N-1. (That is, the time following the last time of the partial time series ₁₂₀₀ -nm is set as the start time of the partial time series 1200- _{nm + 1} ), so that the training data 2000 is created. As a result, the learning data 2000 is created. A correct answer label is attached to each partial time series 1200 constituting the learning data 2000.

Subsequently, the acoustic model learning unit 201 of the acoustic model learning apparatus 20 learns the acoustic model 3000 using the learning data 2000 created by the learning data creating apparatus 10 (step S106). At this time, the acoustic model learning unit 201 cuts out data having the same time width as the frame length T in order from the beginning of the learning data 2000, and then uses these cut-out data and the correct answer label to sound by a known learning algorithm. Learn model 3000. It should be noted that cutting out data having the same time width as the frame length T in order from the beginning of the training data 2000 is a partial time series 1200-n ₁ , a partial time series 1200-n ₂ , ..., Partial time series. It means that 1200-n _N is obtained in order from the training data 2000.

<Summary>
As described above, in the present embodiment, learning composed of a partial time series 1200 subdivided so that the information represented by the voice data 1000 cannot be restored by using the voice data 1000 including personal information, confidential information, and the like. Data 2000 can be created. By using this embodiment, personal information, confidential information, etc. cannot be restored from the learning data 2000, so even the voice data 1000 containing personal information, confidential information, etc. can be used for learning the acoustic model 3000. You will be able to. Therefore, for example, a service provider who provides a service using voice recognition can easily obtain the learning data 2000 necessary for learning the acoustic model 3000, and improve the performance of the acoustic model 3000. Will be possible.

The present invention is not limited to the above-described embodiment disclosed specifically, and various changes and changes, combinations with known techniques, and the like can be made without departing from the scope of claims.

10 Learning data creation device 20 Acoustic model learning device 101 Feature quantity extraction unit 102 Partial time series creation unit 103 Noise addition unit 104 Order rearrangement unit 105 Learning data creation unit 201 Acoustic model learning unit 1000 Audio data 2000 Learning data 3000 Acoustic model

Claims (5)

An extraction method that extracts time-series data of features from the input voice data,
A partial time-series creation means for creating a plurality of partial time-series data by cutting out a feature amount for each predetermined time width from the time-series data extracted by the extraction means.
A noise adding means for adding noise to each of the plurality of partial time series data created by the partial time series creating means,
A learning data creating means for randomly connecting each of a plurality of partial time-series data to which noise is added by the noise adding means to create learning data used for learning an acoustic model.
An information processing device characterized by having. The noise adding means is
A first random number generated for each of the plurality of partial time series data is added as the noise, and the noise is added.
The learning data creation means is
A second random number is generated for each of the plurality of partial time-series data to which noise is added by the noise-adding means, and the plurality of partial time-series data are connected in ascending or descending order of the second random number. The information processing apparatus according to claim 1 , wherein the learning data is created. Having a learning means for learning the acoustic model by using the data obtained by cutting out the learning data created by the learning data creating means at each predetermined time width and the correct answer label given to the data. The information processing apparatus according to claim 1 or 2 . Extraction procedure to extract feature amount time series data from input voice data,
A partial time-series creation procedure for creating a plurality of partial time-series data by cutting out feature quantities at predetermined time widths from the time-series data extracted by the extraction procedure, and
A noise addition procedure for adding noise to each of a plurality of partial time series data created by the partial time series creation procedure, and a noise addition procedure.
A training data creation procedure for randomly connecting each of a plurality of partial time-series data to which noise has been added by the noise addition procedure to create training data used for learning an acoustic model, and a training data creation procedure.
An information processing method characterized by the execution of a computer. A program for making a computer function as each means in the information processing apparatus according to any one of claims 1 to 3 .

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