JP5604464B2 - Vocabulary learning curve parameter estimation apparatus, method, and program - Google Patents

Description

  The present invention relates to a vocabulary learning curve parameter estimation device, method, and program, and more particularly, to a vocabulary learning curve parameter estimation device, method, and program for estimating parameters such as an infant's vocabulary learning speed.

  Although human language development can provide important scientific knowledge and suggestions for thinking about `` what is human beings '', there are many unsolved problems at present, so progress in measurement technology related to language development There is almost no commercial service development. In particular, there has been little progress in science and technology related to vocabulary acquisition, among basic abilities such as speech recognition, vocabulary acquisition, and grammar manipulation. However, technological development in this field is considered to be important in view of the need for education that moderately supports healthy development and the early detection and support of developmental disabilities including language development delays.

  One of the phenomena that is particularly characteristic and important in grasping individuality in the language development of young children is vocabulary learning speed. However, it has been thought that this vocabulary learning speed cannot be obtained without the phenomenon called vocabulary explosion (or vocabulary spurt). Vocabulary explosion is a phenomenon that development psychologists have been paying attention to since the middle of the 20th century, and refers to a sudden change in vocabulary learning speed that occurs in the second half of the year. Basically, toddlers begin to utter their first words around their first birthday, but for a while they learn words at a very moderate rate. However, after the age of one and a half years, the words suddenly start to utter, so the dramatic change has been called “explosion” and “spurt”. The vocabulary explosion is so dramatic that many parents consciously notice it, so it is well known not only in the field of psychology but also in the childcare industry. For this reason, it is thought that the vocabulary explosion must be modeled in order to quantify the state of child vocabulary development.

  Conventionally, in the field of developmental psychology, the phenomenon of vocabulary explosion has been confirmed in multiple languages using large-scale group data using a vocabulary checklist (questionnaire survey based on parents' answers). It has been found that when the average value of the group data is plotted for each age, it becomes a quadratic curve showing a gradual increase, and its inflection point appears around 18-20 months. From these collective data, we have regarded vocabulary explosion as a common phenomenon seen in many children.

  In order to estimate the vocabulary learning speed, it is important for vocabulary explosions to determine when a vocabulary explosion occurs for each individual and how to detect and estimate the vocabulary explosion time (when the vocabulary explosion starts) However, the following four methods for estimating the vocabulary explosion period and estimating the speed of vocabulary learning before and after that have been proposed.

  The first is a visual method in which a graph is drawn without any particular calculation and is visually determined. The second is the 50-word achievement standard method that defines the time when 50 words are learned as the vocabulary explosion time. The third is a specific period achievement standard method in which a period when an achievement standard (for example, 30 words or more) is satisfied in a specific period (for example, three weeks) is set as a vocabulary explosion period. The fourth is a logistic regression approximation method in which the velocity component of the vocabulary acquisition data is approximated to a logistic regression equation, and the inflection point is the vocabulary explosion time (see Non-Patent Document 1).

  Up to now, these methods have been used to determine vocabulary explosion and to determine the vocabulary learning speed before and after that.

Ganger, J., & Brent, M. R. (2004). Reexamining the vocabulary spurt. Developmental Psychology, Vol. 40, No. 4, 621-632.

  However, the first method can confirm the presence or absence of a phenomenon to some extent, but has a problem that the vocabulary speed cannot be automatically determined. The second method is based on empirical data, but based on a very small sample of English-speaking middle classes, there is no guarantee that it will apply to various children in many cultural zones. There is a problem. Another problem is that no individual differences in vocabulary explosion are assumed.

  The third method can detect changes in vocabulary learning speed in a specific time range, but the unique and arbitrary achievement criteria setting takes into account the vocabulary learning speed between individuals. There is a problem that some individuals may misunderstand the vocabulary explosion time completely.

  The fourth method approximates the data for each individual, so it can handle even if the vocabulary learning speed between individuals is different, but it does not accurately capture the characteristics of infant vocabulary development. However, there is a problem that the approximation accuracy of logistic regression is low and the existence of the vocabulary explosion itself cannot often be confirmed. In addition, this method is based on the premise that there is abundant data after the vocabulary explosion, and there is also a problem that it cannot be applied unless the data up to 2 years old are available.

  In order to point out the problems of the fourth method, the vocabulary learning curves for infants are shown in FIGS. This shows the infant's age on the horizontal axis and the cumulative vocabulary acquired on the vertical axis. The fourth method is a complex in which a large discontinuity as shown in FIG. 14B and an S-shaped curve as shown in FIGS. 14C, 15A, and 16A appear. Vocabulary learning curves cannot be modeled. Also, a complicated curve as shown in FIG. 17C could not be modeled.

  As described above, the method that assumes the phenomenon of vocabulary explosion is extremely difficult to model, and it has been impossible to stably obtain the vocabulary learning speed peculiar to an infant using any method.

  In addition, since these methods assume a vocabulary explosion, modeling of vocabulary learning speeds that are completely different before and after the vocabulary explosion is performed, and feature quantities that represent infant characteristics over all periods are extracted. It is not.

  The present invention has been made in view of the above problems, and can calculate a true vocabulary learning curve of an infant and accurately estimate parameters related to vocabulary learning without assuming a vocabulary explosion time. An object of the present invention is to provide a vocabulary learning curve parameter estimation apparatus, method, and program.

  In order to achieve the above object, the vocabulary learning curve parameter estimation device according to the first aspect of the present invention is such that the infant speaks a new word, and the infant speaks before the age. For the time series of data indicating the relationship with the cumulative number of words, for the portion where the age interval until the next new word is spoken is greater than the threshold, the age interval is greater than the threshold. Replacement means for replacing the age of each of the data so as to be a small reference value, and function approximation means for approximating a time series transition of the data using the data replaced by the replacement means with a linear function And the threshold value and the reference value based on the result of replacement by the replacement means and approximation by the function approximation means using each of the plurality of combinations of the threshold value and the reference value Based on the linear function approximated by the function approximating means when using the combination of the threshold value and the reference value selected by the optimal selecting means, the optimal selecting means for selecting the optimal combination, and the infant's Estimating means for estimating the vocabulary learning speed.

  A vocabulary learning curve parameter estimation method according to a second invention is a vocabulary learning curve parameter estimation method in a vocabulary learning curve parameter estimation device including a replacement unit, a function approximation unit, an optimum selection unit, and an estimation unit, and the vocabulary learning curve parameter estimation method The curve parameter estimation device is a data indicating a relationship between an age at which an infant starts to speak a new word and a cumulative number of words that the infant has spoken up to the age by the replacement means. For the time series in which the interval between days until the next new word is spoken is greater than the threshold, the data is set so that the interval between the ages is a reference value smaller than the threshold. Replacing the age of each of the data, and by the function approximating means, the time series of the data using the data replaced by the replacing means A step of approximating a shift with a linear function, and a result of performing replacement by the replacement unit and approximation by the function approximation unit using each of the plurality of combinations of the threshold value and the reference value by the optimum selection unit. Selecting the optimum combination of the threshold value and the reference value, and approximating by the function approximating means when using the combination of the threshold value and the reference value selected by the optimum selecting means by the estimating means. Estimating the vocabulary learning speed of the infant based on the linear function.

  According to the first and second aspects of the invention, the replacement means allows the infant to speak a new word, and the cumulative number of words the infant has spoken before the age. For the time series of data indicating the relationship between and the part where the age interval until the next new word is spoken is greater than the threshold, the age interval is smaller than the threshold and the reference value As such, replace the age of each of the data. The function approximation means approximates the time series transition of the data using the data replaced by the replacement means with a linear function.

  Then, based on the result of the replacement by the replacement unit and the approximation by the function approximation unit using each of the plurality of combinations of the threshold value and the reference value by the optimal selection unit, the optimum of the threshold value and the reference value The right combination. The estimating means estimates the infant's vocabulary learning speed based on the linear function approximated by the function approximating means when the combination of the threshold value and the reference value selected by the optimum selecting means is used.

  Thus, using each of the plurality of combinations of the threshold value and the reference value, the interval of day age until the next new word is spoken is greater than the threshold value, and the interval of day age is the reference value. By replacing each age of the data so that it approximates with a linear function and estimating the vocabulary learning rate based on the approximated linear function using the optimal combination of threshold and reference values, Without assuming the explosion time, it is possible to calculate the true vocabulary learning curve of the infant and stably estimate the vocabulary learning speed accurately.

  The vocabulary learning curve parameter estimation device according to the third aspect of the present invention relates to the relationship between the age at which an infant begins to speak a new word and the cumulative number of words that the infant has spoken before the age. With respect to the time series of data indicating that the interval of days until the next new word is uttered is larger than the threshold, the interval of days is set to a reference value smaller than the threshold. Replacement means for replacing the age of each of the data, function approximation means for approximating a time series transition of the data using the data replaced by the replacement means by a linear function, the threshold value and the reference Based on the result of replacement by the replacement means and approximation by the function approximation means using each of a plurality of combinations of values, an optimal combination of the threshold value and the reference value is selected. And it is configured to include a and optimal selection means for outputting the threshold value.

  A vocabulary learning curve parameter estimation method according to a fourth aspect of the present invention is a vocabulary learning curve parameter estimation method in a vocabulary learning curve parameter estimation device including a replacement unit, a function approximation unit, and an optimum selection unit, and the vocabulary learning curve parameter estimation method The apparatus is a time series of data indicating a relationship between an age at which an infant starts to speak a new word and a cumulative number of words that the infant has spoken up to the age by the replacement means. On the other hand, for the portion where the age interval until the next new word is spoken is larger than the threshold, each of the data is set so that the age interval becomes a reference value smaller than the threshold. The step of replacing the age, and the time series transition of the data using the data replaced by the replacement means by the function approximation means, Approximating with a function, and the threshold value based on a result of replacement by the replacement unit and approximation by the function approximation unit using each of the plurality of combinations of the threshold value and the reference value by the optimum selection unit And selecting an optimal combination of the reference values and outputting the threshold value.

  According to the third and fourth aspects of the invention, the replacement means allows the infant to speak a new word, and the cumulative number of words the infant has spoken before the age. For the time series of data indicating the relationship between and the part where the age interval until the next new word is spoken is greater than the threshold, the age interval is smaller than the threshold and the reference value As such, replace the age of each of the data. The function approximation means approximates the time series transition of the data using the data replaced by the replacement means with a linear function.

  Then, based on the result of the replacement by the replacement unit and the approximation by the function approximation unit using each of the plurality of combinations of the threshold value and the reference value by the optimal selection unit, the optimum of the threshold value and the reference value Select a combination and output a threshold value.

  Thus, using each of the plurality of combinations of the threshold value and the reference value, the interval of day age until the next new word is spoken is greater than the threshold value, and the interval of day age is the reference value. True vocabulary learning for infants without assuming a vocabulary explosion time by replacing each age of the data, approximating with a linear function, and selecting the optimal combination of threshold and reference values Curves can be calculated and vocabulary learning curve parameters can be estimated with good accuracy.

  Moreover, the program of this invention is a program for functioning a computer as each means which comprises said vocabulary learning curve parameter estimation apparatus.

  As described above, according to the vocabulary learning curve parameter estimation device, method, and program of the present invention, the true vocabulary learning curve of the infant is calculated without assuming the vocabulary explosion time, and the vocabulary learning speed is stably obtained. Alternatively, it is possible to estimate the vocabulary learning curve parameter with high accuracy.

It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a block diagram which shows the functional structure of the vocabulary learning curve parameter estimation apparatus of this Embodiment. It is a figure which shows an example of an input screen. It is a figure which shows an example of an input data set. It is a flowchart which shows the content of the vocabulary learning curve parameter estimation processing routine in the vocabulary learning curve parameter estimation apparatus of this Embodiment. It is a flowchart which shows the flow of the process which removes the plateau in the vocabulary learning curve parameter estimation apparatus of this Embodiment. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve. It is a graph which shows an infant's vocabulary learning curve.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

<Outline of the invention>
First, the natural law regarding vocabulary learning discovered by the inventors will be explained. Inventors have discovered that in FIG. 14 to FIG. 17, the vocabulary learning curve frequently occurs during a certain period (for example, 6 days or more) during which no word is learned. The part of the learning curve that does not remember one word for more than a certain period of time will be called a plateau (a technical term in learning psychology that means “plain”).

  Our model of vocabulary learning curve assumes that there is a true learning curve in vocabulary learning for infants, and this plateau is frequently inserted in the early stages of vocabulary learning, so the phenomenon of vocabulary explosion is observed. It is said that.

  In order to demonstrate this, the plateau portion was replaced by a certain period (for example, 1 day) (plateau removal) and the vocabulary learning curve was redrawn. As a result, it was found that the vocabulary learning curves were aligned. The inventors assume that this curve is a true vocabulary learning curve. The state is shown in FIGS. -Is the vocabulary learning curve shown in FIGS. 14 to 17, and + indicates the start of a plateau. ○ indicates the learning curve after plateau removal. The solid line represents a straight line that approximates the learning curve. When the plateau is removed in this way, the learning curve is almost a straight line, and its slope and intercept show characteristics specific to infants. The natural phenomena discovered here are summarized as follows.

(1) The plateau lasts from the beginning of vocabulary learning to the end of the period previously called vocabulary explosion, and then decreases in number and length.

(2) Except for the plateau, the complicated vocabulary learning curve that seems to be due to the vocabulary explosion can be modeled with a straight line.

  In the present invention, using this newly discovered natural phenomenon, a vocabulary learning curve unique to an infant that does not depend on vocabulary explosion is obtained, and the vocabulary learning speed is obtained from the curve.

<System configuration>
As shown in FIG. 9, the vocabulary learning curve parameter estimation device 10 according to the present exemplary embodiment includes an input unit 12 that receives input of various data, a calculation unit 14 that estimates parameters related to vocabulary learning, and outputs an estimation result. And an output unit 16.

  The input unit 12 is realized by an input device such as a known keyboard, mouse, or storage device, and receives input data.

  Here, in order to estimate an infant's vocabulary learning speed, what kind of data is referred to is a problem first. If it is possible to record all of the infant's utterances with electronic media such as a digital video recorder, it can be said that the most accurate method is to analyze it, but the cost of data acquisition is enormous and the infant's ambiguous utterance data Since there is no engineering technology that automatically recognizes and analyzes at the word level, it is very difficult to realize. On the other hand, there is also a method in which a questionnaire is answered every predetermined period (for example, once every three months) to grasp a change in the number of words newly spoken by an infant. In this case, if the predetermined period is long, it is difficult to grasp the exact timing of the vocabulary explosion. In addition, if the predetermined period is short, the burden on the respondent of the questionnaire (the parent of the infant) increases. Therefore, in practice, it is desirable to have a method capable of acquiring data at fine time points while reducing the burden on the parent who records the data.

  Therefore, in the present embodiment, data acquisition using the web diary method is applied. In this method, when an infant learns a new word (speaks), a specific site on the web is accessed via a network from a mobile phone or a personal computer, and the word that the infant remembers is recorded along with the diary of the day. (Non-Patent Document 2 “Tetsuo Kobayashi, Masaaki Nagata (2009),“ Infant Language Development Research Using the Web: Trial of Large-Scale Longitudinal Data Collection ”, Proc. 15th Annual Conference of the Language Processing Society, pp. 534-537., Non-Patent Document 3, “Tetsuo Kobayashi, Masaaki Nagata (March 2010),“ Reliability Verification of Infant Vocabulary Development Data Collected on the Web ”, 16th Annual Conference of the Language Processing Society of Japan See the collection of papers, pages 403-406.). The effectiveness of this method is very good in that it has been scientifically verified.

  In addition, the advantage of data acquisition by this method is that it is relatively easy for parents to record, but the date of recording (the date when the infant learned a new word) and the date of birth of the infant From the difference, it is possible to calculate the age at which the infant learned a new word. By using the data acquired in this way, the vocabulary learning curve parameter estimation apparatus 10 of the present embodiment can estimate how many words are learned per day.

  For example, an input screen 50 as shown in FIG. 10 is provided in the input unit 12 as an input interface to input data. In the input screen 50 of FIG. 10, a date input area 52, a word input area 54, a date of birth display area 56, and a registration correction button 58 are provided.

  In the date input area 52, the date (word acquisition date) when the infant spoke a new word can be input by direct input or selection from a pull-down menu. Further, when the input screen 50 is opened, the date of the day may be input as an initial value. In the word input area 54, the utterance and meaning of a word newly learned by the infant can be input by direct input. The date of birth display area 56 displays the date of birth of the infant registered in advance. When the date of birth is not registered, or when the registered date of birth is to be corrected, by pressing the registration correction button 58, the date of birth input screen is displayed and the input of the date of birth is accepted.

  By accepting the input data in this way, as shown in Fig. 11, when (eg: September 12, 2009) what word (eg: doggie) and what meaning (eg: dog) was spoken A data set composed of the date of birth, the date of word acquisition, the utterance, and the meaning is obtained.

  The computing unit 14 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and a ROM (Read Only Memory) that stores a program for executing a vocabulary learning curve parameter estimation processing routine described later. It consists of Functionally, the calculation unit 14 functionally includes a word acquisition age calculation unit 20, a data set storage unit 22, a plateau removal unit 24, a plateau data storage unit 26, a function approximation unit 28, and an optimum parameter selection unit 30. And a vocabulary learning speed calculation unit 32. The plateau removal unit 24 is an example of a replacement unit, and the vocabulary learning speed calculation unit 32 is an example of an estimation unit.

The word acquisition age calculation unit 20 indicates the date of acquisition of each word from the difference between the word acquisition date and the date of birth in the data set input from the input unit 12. Calculate age. For example, if the word acquisition date is “September 12, 2009” and the date of birth is “September 12, 2008”, the age of acquisition = September 12, 2009-September 12, 2008 = 365 days old. The calculated acquisition ages of the words are arranged in ascending order, and an integer series of 1, 2, 3,... Is assigned in ascending order to calculate the cumulative number of words (the most remembered word). Thereby, a time series of a data set including a set of the acquired age x _i and the cumulative word number y _i is generated and stored in the data set storage unit 22.

  In addition, it is good also as a format which acquires in the input part 12 the data set which consists of a set of acquisition age and the cumulative number of words directly. In this case, the configuration of the word acquisition age calculation unit 20 can be omitted in the calculation unit 14. Moreover, it is good also as a format which acquires the data set previously accumulate | stored in the memory | storage device.

The data set storage unit 22 stores a time series of a data set including a set of the acquisition date age x _i and the cumulative word number y _i generated by the word acquisition age calculation unit 20.

The plateau removing unit 24 is a plateau in which the interval between days until the next new word is spoken from the time series of the acquired age and cumulative vocabulary data sets acquired from the data set storage unit 22 is greater than p. (day intervals old) it is, so that all the reference value p ', replacing the acquisition date age x _i of each data set. As a result, a time series of a data set of new acquired age x _i ′ and cumulative vocabulary number y _i is obtained.

  Here, a threshold value for the length of the plateau to be removed is set as p, a reference plateau for replacing the plateau is set as p ′, and a plurality of combinations of p and p ′ are generated and stored in the plateau data storage unit 26 in advance. Keep it. The combination of p and p 'is generated by changing each of p and p'.

Plateau removal unit 24, p and p 'with each combination of performs replacement acquisition date age x _i of each data set, p and p' for each combination of a new acquisition date age x _i '(p , P ′) and the time series of the data set of the cumulative vocabulary number y _i .

For each combination of p and p ′, the function approximating unit 28 replaces the acquired age x _i ′ (p, p ′) and the cumulative number of words y using the combination of p and p ′ by the plateau removing unit 24. A time series of a data set composed of a pair with _i is plotted in a coordinate system in which the cumulative number of words is the y-axis and the acquired age is the x-axis, and each plotted data point is approximated by a function.

Specifically, a function y = f _{p, p ′} (x ′ (p, p ′)) that approximates the relationship between the acquired age x _i ′ (p, p ′) and the cumulative number of words y _i. Assuming In the present embodiment, a straight line such as a linear function y = ax ′ + b is assumed. These a and b can be obtained by the least square method as shown in the following equation (1).

  Here, the square distance between the data and the straight line is used as the norm, but another norm may be used.

As described above, the function approximating unit 28 converts the time series of the data set replaced by the plateau removing unit 24 to the function y = f _{p, p ′} (x ′ (p, p ′)) for each combination of p and p ′. ) To obtain approximate results.

For each combination of p and p ′, the optimum parameter selection unit 30 calculates the function y = a _{p, p ′} x ′ (p, p ′) + b _{p, p ′} approximated by the function approximation unit 28 and the time series of the data set. The distance to y _i (for example, the sum of squares of the difference between the data and the straight line) is calculated, and the combination of p and p ′ corresponding to the function that minimizes the calculated distance is selected as the optimum parameter.

The vocabulary learning speed calculation unit 32 uses the function y = f _{p, p ′} (x ′ (p, p ′)) approximated by the function approximation unit 28 when the selected optimum parameters p and p ′ are used. Differentiate by 'and calculate the vocabulary learning speed. In the example using the above equation (1), the vocabulary learning speed has a slope a _{p, p ′} and is constant regardless of time.

The output unit 16 is mounted with a display, a printer, a magnetic disk, or the like, and the calculation result of the calculation unit 14 is output. For example, the optimum parameter p selected by the optimum parameter selection unit 30 is output as the optimum threshold for the plateau length, and the vocabulary learning speed calculated by the vocabulary learning speed calculation unit 32 is output. The output is not limited to this. For example, a combination of the optimum parameter p selected by the optimum parameter selection unit 30 and a reference plateau p ′ that replaces the plateau may be outputted. In addition to the vocabulary learning speed calculated by the vocabulary learning speed calculation unit 32, the function y = f _{p, p ′} (x ′ ( p, p ′)) y-intercept (parameter b) and x-intercept may be output.

<Operation of Vocabulary Learning Curve Parameter Estimation Device>
Next, a vocabulary learning curve parameter estimation processing routine executed in the vocabulary learning curve parameter estimation apparatus 10 of the present embodiment will be described with reference to FIG.

  In step S100, a data set composed of a date of birth, a word acquisition date, an utterance, and a meaning is acquired.

Next, in step S102, the acquisition date of each word is calculated from the difference between the date of acquisition and the date of birth of the data set acquired in step S100. Then, the calculated acquisition ages of the words are arranged in ascending order, and an integer series of 1, 2, 3,... Is assigned from the smallest to calculate the cumulative number of words. Thereby, a time series of a data set composed of a set of the acquired age x _i and the cumulative word number y _i is generated and stored in the data set storage unit 22.

  In the next step S104, any one combination p, p of all the combinations of the threshold value p of the length of the plateau to be removed and the reference value p ′ for replacing the plateau stored in the plateau data storage unit 26. Set '. For example, the range of the value of p is assumed to be a discrete value (integer) of 4 to 15, the range of the value of p ′ is assumed to be a discrete value (integer) of 1 to 3, and the range of the value of p And the combinations p and p ′ obtained by brute force in the range of the values of p and p ′ are stored in the plateau data storage unit 26.

  In the next step S106, each data set is replaced so as to remove the plateau from the time series of the data set generated in step S102 using p and p 'set in step S104.

  Here, step S106 is realized by the processing routine shown in FIG.

First, the length L of the time series sequence and the data set when the data set acquisition date age x _i and the cumulative vocabulary y _i is obtained. Further, the set combination p, p ′ is set as a threshold value of the plateau length to be removed and a reference plateau for replacing the plateau.

In step S120, the variable i indicating the time-series order of the data set is set to 1, and S for calculating the cumulative value of the plateau is set to 0. In step S122, it is subtracted from x _i entered the set accumulated plateau S, and records replaced with new Nichiyowai x _i '.

In step S124, it is determined whether i is less than length L. If i is less than length L, it is determined that there is a next data set. In step S126, the next data is set. The difference between the set age x _{i + 1} and the current age x _i (the age interval) is compared with the threshold value p, and if the difference in age is greater than p, in step S128, the cumulative Let the plateau be S = S + x _{i + 1} −x _i −p ′. This determines how much value should be subtracted from the age of each subsequent data set to replace a plateau greater than p (the age interval) with p ′.

  In step S130, i is incremented by 1, and the process returns to step S122. In this way, the operations in steps S122 to S128 are repeated for all L data sets.

If it is determined in step S124 that i is greater than or equal to the length L, it is determined that there is no next data set, and in step S132, all x _j ′ and y _j recorded in step S122 are output. Then, the processing routine is finished.

According to the above processing routine, an updated value of the data set of acquired age x _i ′ and cumulative vocabulary number y _i is obtained for the set combination p, p ′.

Then, in step S108 in FIG. 12, from the set of acquired age x _i ′ and cumulative vocabulary number y _i replaced so as to remove the plateau using the combination p, p ′ set in step S106. The time series of the data set is plotted in a coordinate system with the cumulative number of words on the y-axis and the acquired age on the x-axis so that the norm with each plotted data point is minimized according to the above equation (1). A linear function (y = ax + b) is obtained.

  In the next step S110, it is determined whether or not the processing of steps S104 to S108 has been executed for all the combinations p and p ′ stored in the plateau data storage unit 26, and the processing of steps S104 to S108 is executed. If there is an uncombined combination p, p ′, the process returns to step S104, and the combination p, p ′ is set. On the other hand, when the processes of steps S104 to S108 are executed for all combinations p and p 'stored in the plateau data storage unit 26, the process proceeds to step S112.

  In step S112, the linearity approximated to the time series of the data set is obtained for every combination p and p ′ based on the result of approximation with the linear function obtained in step S108 for all the combinations p and p ′. The distance to the function is calculated, and the optimal combination p, p ′ is selected.

  Next, in step S114, the value of the slope a of the linear function obtained in step S108 when the optimum combination p, p 'selected in step S112 is used is calculated as the vocabulary learning speed. In step S116, the vocabulary learning speed calculated in step S114 is output, and the optimal combination p, p ′ selected in step S112 is output as an optimal threshold value for the plateau length. Then, the process ends.

  Table 1 shows the vocabulary learning speed obtained by estimating the vocabulary learning speed using the actual data of the 15 speakers shown in FIGS. However, the range of p in the combination of p and p ′ was 4 to 15, and the range of p ′ was 1 to 3.

  As described above, according to the vocabulary learning curve parameter estimation device of the present embodiment, the date until the next new word is spoken using each of a plurality of combinations of the plateau threshold value and the reference value. For plateaus where the age interval is greater than the threshold, replace each day of the data set so that the age interval is the reference value, approximate with a linear function, and use the optimal combination of threshold and reference values By estimating the slope of the approximated linear function as the vocabulary learning speed, the child's true vocabulary learning curve can be calculated and the vocabulary learning speed can be estimated accurately and stably without assuming the vocabulary explosion time. Can do. In addition, by selecting the optimal combination of threshold and reference value, the child's true vocabulary learning curve is calculated without assuming the vocabulary explosion time, and the optimum threshold for the plateau length is accurately calculated. It can be estimated well.

  For a plateau where the age interval before the next new word begins to be spoken is greater than the threshold, replace each age in the data set so that the age interval is the reference value, and then a linear function And estimating the slope of the approximated linear function as the vocabulary learning speed, the vocabulary learning speed can be stably estimated with high accuracy without assuming the vocabulary explosion time.

  In addition, the plateau is removed from the data indicating the relationship between the age at which the infant began speaking a new word and the cumulative number of words, the vocabulary learning curve is approximated by a function, and the vocabulary learning speed is estimated from the slope. . Thereby, a true vocabulary learning speed that does not depend on the vocabulary explosion can be obtained. One of the effects of estimating this vocabulary learning speed is that not only can you estimate the true vocabulary learning speed from a small number of data at the beginning of vocabulary learning (for example, the first 20 words you learned), but you can also use this period of data It can be used to estimate the vocabulary learning speed. By using such vocabulary learning speed unique to infants, it is possible to make tailored education tailored to each individual's characteristics and development more effectively, and it can be a very valuable index in commerce.

  In the above embodiment, the case where all the combinations of p and p ′ are stored in the plateau data storage unit has been described as an example. However, only the range of p values and the range of p ′ values are stored. You may make it keep. In this case, various combinations of p and p 'may be generated by changing p and p' within the range of p and p '.

  The present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the gist of the present invention.

  In addition, the vocabulary learning curve parameter estimation device described above has a computer system inside, but if the “computer system” uses a WWW system, a homepage providing environment (or display environment) is also available. Shall be included.

  In the present specification, the embodiment has been described in which the program is installed in advance. However, the program can be provided by being stored in a computer-readable recording medium.

DESCRIPTION OF SYMBOLS 10 Vocabulary learning curve parameter estimation apparatus 12 Input part 14 Operation part 16 Output part 20 Word acquisition age calculation part 22 Data set storage part 24 Plateau removal part 26 Plateau data storage part 28 Function approximation part 30 Optimal parameter selection part 32 Vocabulary learning speed Calculation part

Claims (6)

Next to the time series of data indicating the relationship between the age at which the infant began speaking a new word and the cumulative number of words spoken by the infant until the age, the next new word A replacement means for replacing each age of the data so that the interval between the ages before the utterance becomes greater than a threshold value becomes a reference value smaller than the threshold value,
Function approximation means for approximating a time series transition of the data using the data replaced by the replacement means with a linear function;
Optimal selection for selecting an optimal combination of the threshold value and the reference value based on the result of replacement by the replacement means and approximation by the function approximation means using each of the plurality of combinations of the threshold value and the reference value Means,
Estimating means for estimating the vocabulary learning speed of the infant based on the linear function approximated by the function approximating means when the combination of the threshold value and the reference value selected by the optimum selecting means is used;
Vocabulary learning curve parameter estimation device including Next to the time series of data indicating the relationship between the age at which the infant began speaking a new word and the cumulative number of words spoken by the infant until the age, the next new word A replacement means for replacing each age of the data so that the interval between the ages before the utterance becomes greater than a threshold value becomes a reference value smaller than the threshold value,
Function approximation means for approximating a time series transition of the data using the data replaced by the replacement means with a linear function;
Based on the result of the replacement by the replacement unit and the approximation by the function approximation unit using each of the plurality of combinations of the threshold value and the reference value, the optimum combination of the threshold value and the reference value is selected, Optimal selection means for outputting the threshold;
Vocabulary learning curve parameter estimation device including   The function approximating unit is configured to minimize the square sum of the difference between the linear function approximated by the function and each data with respect to the time series transition of the data using the data replaced by the replacing unit. The vocabulary learning curve parameter estimation apparatus according to claim 1 or 2, wherein a time series transition of the data is approximated by the linear function. A vocabulary learning curve parameter estimation method in a vocabulary learning curve parameter estimation device including a replacement means, a function approximation means, an optimum selection means, and an estimation means,
The vocabulary learning curve parameter estimation device comprises:
With respect to the time series of data indicating the relationship between the age at which the infant began to speak a new word by the replacement means and the cumulative number of words that the infant began to speak before the age The age of each of the data is set such that the interval between the ages until the next new word is spoken is greater than the threshold, so that the age interval becomes a reference value smaller than the threshold. A replacement step;
Approximating a time series transition of the data using the data replaced by the replacing means by a linear function by the function approximating means;
Based on the result of the replacement by the replacement unit and the approximation by the function approximation unit using each of the plurality of combinations of the threshold value and the reference value by the optimal selection unit, the optimum of the threshold value and the reference value is determined. Selecting a combination;
The estimation means estimates the infant vocabulary learning speed based on the linear function approximated by the function approximation means when the combination of the threshold value and the reference value selected by the optimum selection means is used. Steps,
Vocabulary learning curve parameter estimation method executed including A vocabulary learning curve parameter estimation method in a vocabulary learning curve parameter estimation device including a replacement means, a function approximation means, and an optimum selection means,
The vocabulary learning curve parameter estimation device comprises:
With respect to the time series of data indicating the relationship between the age at which the infant began to speak a new word by the replacement means and the cumulative number of words that the infant began to speak before the age The age of each of the data is set such that the interval between the ages until the next new word is spoken is greater than the threshold, so that the age interval becomes a reference value smaller than the threshold. A replacement step;
Approximating a time series transition of the data using the data replaced by the replacing means by a linear function by the function approximating means;
Based on the result of the replacement by the replacement unit and the approximation by the function approximation unit using each of the plurality of combinations of the threshold value and the reference value by the optimal selection unit, the optimum of the threshold value and the reference value is determined. Selecting a combination and outputting the threshold;
Vocabulary learning curve parameter estimation method executed including   The program for functioning a computer as each means which comprises the vocabulary learning curve parameter estimation apparatus of any one of Claims 1-3.