Patent Publication Number: US-2021177340-A1

Title: Cognitive function evaluation device, cognitive function evaluation system, cognitive function evaluation method, and storage medium

Description:
TECHNICAL FIELD 
     The present disclosure relates to a cognitive function evaluation device, a cognitive function evaluation system, a cognitive function evaluation method, and a storage medium. 
     BACKGROUND ART 
     Typical tests for evaluating cognitive functions include the Hasegawa dementia scale-revised (HDS-R), the mini-mental state examination (MMSE), and the clinical dementia rating (CDR) that cause evaluatees being suspected patients, whose cognitive functions are to be evaluated, to answer questions on test papers. These methods are used for evaluatees in medical institutions by doctors, clinical psychologists, or other practitioners trained to some extent. 
     Such an evaluation method using test papers requires a long test time, that is, a burden on evaluatees. Repeatedly taking the same test, evaluatees may remember the answers. To solve these problems, disclosed is a technique that a doctor or any other practitioner records the voice of an evaluatee answering questions in a test and analyzes the voice of the evaluatee (see, e.g., Patent Literature (PTL) 1). 
     CITATION LIST 
     Patent Literature 
     PTL 1: International Publication No. WO2012/165602 
     SUMMARY OF THE INVENTION 
     Technical Problem 
     There is a demand for simple and accurate evaluation on the cognitive function of an evaluatee. 
     To meet the demand, it is an objective of the present disclosure to provide a cognitive function evaluation device, for example, capable of simply and accurately evaluating the cognitive function of an evaluatee. 
     Solutions to Problem 
     A cognitive function evaluation device according to an aspect of the present disclosure includes: an obtainment unit configured to obtain utterance data indicating a voice of an evaluatee uttering a sentence as instructed; a calculation unit configured to calculate, from the utterance data obtained by the obtainment unit, a feature based on the utterance data; an evaluation unit configured to compare the feature calculated by the calculation unit to reference data indicating a relationship between voice data indicating a voice of a person and a cognitive function of the person to evaluate the cognitive function of the evaluatee; and an output unit configured to output the sentence to be uttered by the evaluatee and output a result of the evaluation by the evaluation unit. 
     A cognitive function evaluation system according to an aspect of the present disclosure includes: the cognitive function evaluation device; a voice collection device that detects the voice of the evaluatee; and a display device that displays the sentence and the result of the evaluation output by the output unit. 
     A cognitive function evaluation method according to an aspect of the present disclosure is executed by a computer. The cognitive function evaluation method includes: outputting a sentence to be uttered by an evaluatee; obtaining utterance data indicating a voice of the evaluatee uttering the sentence; calculating, from the utterance data obtained in the obtaining, a feature based on the utterance data; evaluating a cognitive function of the evaluatee by comparing the feature calculated in the calculating to reference data indicating a relationship between voice data indicating a voice of a person and a cognitive function of the person; and outputting a result of the evaluating. 
     The present disclosure may be implemented as a non-transitory computer-readable storage medium storing a program for causing the computer to execute the cognitive function evaluation method. 
     Advantageous Effect of Invention 
     A cognitive function evaluation device, for example, according to the present disclosure allows simple and accurate evaluation on the cognitive function of an evaluatee. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  shows a configuration of a cognitive function evaluation system according to an embodiment. 
         FIG. 2  is a block diagram showing characteristic functional configurations of a cognitive function evaluation device and the cognitive function evaluation system according to the embodiment. 
         FIG. 3  is a flowchart showing a processing procedure of the cognitive function evaluation device according to the embodiment evaluating the cognitive function of an evaluatee. 
         FIG. 4  shows an example method of obtaining the utterance data on the evaluatee using an obtainment unit. 
         FIG. 5  shows example utterance data indicating the voice of the evaluatee. 
         FIG. 6  illustrates a formant frequency calculated from the utterance data by a calculation unit. 
         FIG. 7  shows example time changes in formant frequencies calculated from the utterance data by the calculation unit. 
         FIG. 8  illustrates an example feature of the utterance data evaluated by an evaluation unit. 
         FIG. 9  illustrates another example feature of the utterance data evaluated by the evaluation unit. 
         FIG. 10  shows scores acquired by people in the MoCA test. 
         FIG. 11  shows an example image displayed on a display device and indicating a decline in the cognitive function. 
         FIG. 12  shows another example image displayed on the display device in obtaining utterance data on the evaluatee. 
         FIG. 13  shows a configuration of a cognitive function evaluation system according to Variation 1 of the embodiment. 
         FIG. 14  shows a configuration of a cognitive function evaluation system according to Variation 2 of the embodiment. 
     
    
    
     DESCRIPTION OF EXEMPLARY EMBODIMENT 
     Now, an embodiment will be described with reference to the drawings. Note that the embodiment described below is a mere comprehensive or specific example of the present disclosure. The numerical values, shapes, materials, constituent elements, the arrangement and connection of the constituent elements, steps, step orders etc. shown in the following embodiment are thus mere examples, and are not intended to limit the scope of the present disclosure. Among the constituent elements in the following embodiment, those not recited in any of the independent claims defining the broadest concept of the present disclosure are described as optional constituent elements. 
     The figures are schematic representations and not necessarily drawn strictly to scale. In the figures, substantially the same constituent elements are assigned with the same reference marks, and redundant descriptions will be omitted or simplified. 
     The following embodiment employs expressions for directions. For example, “horizontal” means not only “completely horizontal” but also “substantially horizontal”, that is, including differences of about several percent, for example. 
     Embodiment 
     Configuration of Cognitive Function Evaluation Device 
     A configuration of a cognitive function evaluation system according to the embodiment will be described.  FIG. 1  shows the configuration of the cognitive function evaluation system according to the embodiment. 
     Cognitive function evaluation system  200  is for evaluating the cognitive function of evaluatee U from the voice of evaluatee U. The cognitive function represents capabilities such as recognition, remembering, or judgment. As a specific example, cognitive function evaluation device  100  evaluates whether evaluatee U has dementia (i.e., whether the evaluatee is a dementia patient). 
     The symptoms of dementia include a decline in the cognitive function described above. Specific examples of dementia include Alzheimer&#39;s disease (AD). Since dementia patients are often not aware of any symptoms, the family of a suspected dementia patient or a third person encourages him/her to receive a medical examination at a hospital. Only then, the suspected patient sees a doctor. Alternatively, evaluatee U takes a batch test for dementia, such as the Montreal cognitive assessment (MoCA) test, to check whether evaluatee U has dementia. 
     The MoCA test takes, however, about 15 minutes each time. The MoCA test needs to be conducted a plurality of times at an interval to examine evaluatee U&#39;s change over time, thereby determining whether evaluatee U has dementia. That is, one set of the MoCA test requires a long time to examine whether evaluatee U has dementia. 
     It is known that there tends to be a difference in the voice between dementia patients and non-dementia people (i.e., healthy people), even when they utter the same word. 
     Cognitive function evaluation system  200  analyzes the voice of evaluatee U, thereby accurately evaluating the cognitive function level of evaluatee U. 
     As shown in  FIG. 1 , cognitive function evaluation system  200  includes cognitive function evaluation device  100 , voice collection device  300  that detects the voice of evaluatee U, and display device  400 . 
     Cognitive function evaluation device  100  is a computer that evaluates the cognitive function of evaluatee U from utterance data (i.e., voice data) obtained by voice collection device  300  and indicating the voice of evaluatee U. Specifically, cognitive function evaluation device  100  causes display device  400  to display sentence data indicating a certain sentence to be uttered by evaluatee U (i.e., image data including the sentence). The device obtains utterance data indicating the voice of evaluatee U via voice collection device  300 . 
     Voice collection device  300  is a microphone that detects the voice of evaluatee U and outputs utterance data indicating the detected voice to cognitive function evaluation device  100 . In order to accurately detect the voice of evaluatee U, at least one of isolation shield  310  or pop guard  320  may be arranged around voice collection device  300 . 
     Display device  400  displays images based on image data output from cognitive function evaluation device  100 . Specifically, display device  400  obtains and displays sentence data and the result of the evaluation on the cognitive function of evaluatee U. The sentence data is output from output unit  140  (see, e.g.,  FIG. 2 ), which will be described later, and indicates the sentence to be uttered by evaluatee U. Specifically, display device  400  is a monitor device such as a liquid crystal panel or an organic EL panel. Display device  400  may be an information terminal such as a television, a smartphone, or a tablet terminal. 
     Cognitive function evaluation device  100 , voice collection device  300 , and display device  400  may be connected in a wired or wireless fashion, as long as capable of sending and receiving utterance data or image data. 
     Cognitive function evaluation device  100  analyzes the voice of evaluatee U based on the utterance data detected by voice collection device  300 , evaluates the cognitive function of evaluatee U from the result of the analyzation, and outputs an image indicating the result of the evaluation to display device  400 . This configuration causes cognitive function evaluation device  100  to notify a dementia patient, who is not aware of any symptoms, of the cognitive function level and thus to encourage the dementia patient, for example, to see a doctor. In other words, cognitive function evaluation device  100  notifies a dementia patient, who is not aware of any symptoms, of the cognitive function level, thereby encouraging the dementia patient to see a doctor. 
     Note that cognitive function evaluation device  100  is a personal computer, for example, but may be a server device. 
       FIG. 2  is a block diagram showing a characteristic functional configuration of cognitive function evaluation device  100  according to the embodiment. Cognitive function evaluation device  100  includes obtainment unit  110 , calculation unit  120 , evaluation unit  130 , output unit  140 , storage unit  150 , and instruction unit  160 . 
     Obtainment unit  110  obtains utterance data indicating the voice of evaluatee U uttering a sentence as instructed. Specifically, the obtainment unit obtains the utterance data detected by voice collection device  300 . Obtainment unit  110  is a communication interface that performs wired or wireless communications, for example. 
     Calculation unit  120  is a processing unit that analyzes the utterance data on evaluatee U obtained by obtainment unit  110  to calculate a feature based on the utterance data. Specifically, calculation unit  120  may be configured as hardware using a processor, a microcomputer, or a dedicated circuit. 
     For example, the sentence indicated by the sentence data output from output unit  140  contains a character string with consecutive syllables, each of which consists of a vowel. Calculation unit  120  may calculate, as the feature, at least one of the amounts of changes in the first and second formant frequencies of the vowel, the times required for the changes in the first and second formant frequencies of the vowel, or the rates of changes that are the ratios of the amounts of changes to the required times. 
     The first formant frequency is a peak frequency of the amplitude that can be seen first, counting from the lowest frequency of the human voice. It is known that the first formant frequency tends to reflect the feature related to the movement of the tongue. As compared to healthy people, dementia patients often fail to move their tongue well. It is thus considered that there tends to be a difference in the first formant frequency between healthy people and dementia patients. 
     The second formant frequency is a peak frequency of the amplitude that can be seen second, counting from the lowest frequency of the human voice. It is known that the second formant frequency tends to reflect the influence related to the position of the tongue, out of the resonance caused by the vocal cord sound source in the vocal tract, the nasal cavity, and the oral cavity such as lips, and the tongue. As compared to healthy people, dementia patients often suffer from a decline in the motor function maintaining the position of the tongue or the chin. It is thus considered that there tends to be a difference in the second formant frequency and the amplitude between healthy people and dementia patients. 
     For example, the sentence indicated by the sentence data output from output unit  140  may contain a plurality of syllables, each of which includes a vowel. Calculation unit  120  may calculate, as the feature, a variation in at least one of the first formant frequency of the vowel, the second formant frequency of the vowel, or the ratio of the second formant frequency of the vowel to the first formant frequency of the vowel. The degree of variation calculated as the feature is, for example, a standard deviation. 
     For example, the sentence indicated by the sentence data output from output unit  140  may contain at least three syllables including different vowels. Calculation unit  120  may calculate, as the feature, at least one of the shape or the area of the polygon defined by plotting the value of the second formant frequency with respect to the first formant frequency calculated from each of the at least three vowels in the coordinate space defined by the second formant frequency of the vowel with respect to the first formant frequency of the vowel. 
     For example, the sentence indicated by the sentence data output from output unit  140  may contain at least two syllables including different vowels. Calculation unit  120  may calculate, as the feature, the positional relationship when plotting the value of the second formant frequency with respect to the first formant frequency calculated from the at least two vowels in the coordinate space defined by the second formant frequency of the vowel with respect to the first formant frequency of the vowel. 
     For example, the sentence indicated by the sentence data output from output unit  140  may contain a syllable including a consonant and a vowel subsequent to the consonant (i.e., a subsequent vowel). Calculation unit  120  may calculate, as the feature, the difference in the voice pressure between the consonant and the subsequent vowel. Note that, in this specification, the subsequent vowel is the phoneme that is a vowel such as “a”, “i”, “u”, “e”, or “o” subsequent to the phoneme that is a consonant such as “k”, “s”, “t”, “n”, or “h”. For example, “ta” includes “t” as a consonant and “a” as a subsequent vowel following the consonant. For example, in the case of “ta”, the difference in the voice pressure between the consonant and the subsequent vowel corresponds to the difference between the voice pressure of “t” and the voice pressure of “a”. Examples of the “consonant and the subsequent vowel following the consonant” include what is called “open syllables” except for syllables, each of which consists of a vowel only. In particular, the consonant employed in calculating the feature may be a stop consonant (i.e., what is called a “plosive”) such as “p”, “t”, or “k” that tends to cause a difference between healthy people and dementia patients. 
     For example, calculation unit  120  may calculate, as the feature, the time required by evaluatee U to utter the sentence. 
     For example, output unit  140  may further output an instruction that causes evaluatee U to utter a sentence a plurality of times. Calculation unit  120  may calculate, as the feature, the amount of change in the reading time calculated from reading times obtained for the plurality of times U evaluatee uttered the sentence. 
     Evaluation unit  130  compares a plurality of features that are ones calculated by calculation unit  120  or selected freely to reference data  151  stored in storage unit  150  to evaluate the cognitive function of evaluatee U. For example, storage unit  150  stores, as reference data  151 , a threshold of a feature for distinguishing healthy people, mild dementia patients, and dementia patients from each other. Evaluation unit  130  compares the feature calculated by calculation unit  120  and the threshold stored as reference data  151  to evaluate that evaluatee U is a healthy person, a mild dementia patient, or a dementia patient. Evaluation unit  130  is a processor, a microcomputer, or a dedicated circuit, for example. Note that calculation unit  120  and evaluation unit  130  may be integrated in a single processor, a microcomputer, or a dedicated circuit with corresponding functions, or may be achieved by a combination of two or more of processors, microcomputers, and dedicated circuits. 
     Output unit  140  outputs sentence data (i.e., image data) indicating a certain sentence to be uttered by evaluatee U and the result of the evaluation on the cognitive function of evaluatee U by evaluation unit  130  to display device  400 . Output unit  140  is a communication interface that performs wired or wireless communications, for example. For example, the sentence indicated by the sentence data output from output unit  140  may contain a character string of at least one of consecutive syllables, each of which consists of a consonant and a vowel subsequent to the consonant (i.e., a subsequent vowel), or consecutive syllables, each of which consists of a vowel only. That is, the sentence indicated by the sentence data output from output unit  140  may contain a character string of consecutive open syllables. 
     For example, the sentence indicated by the sentence data output from output unit  140  may contain five or more characters, each of which consists of a stop consonant and a subsequent vowel. Specifically, the sentence indicated by the sentence data output from output unit  140  may contain at least one of character strings “Kitakaze to taiyo ga deteimasu”, “Tankenka wa bouken ga daisuki desu”, or “Kita kara kita kata tatakiki”. 
     Storage unit  150  is a storage device that stores reference data  151  indicating the relationship between a feature based on voice data indicating the voices of people and the cognitive functions of the people. Reference data  151  is referenced by evaluation unit  130  in evaluating the cognitive function of evaluatee U and stored in advance in storage unit  150 . Storage unit  150  is read-only memory (ROM), a random-access memory (RAM), for example. 
     Storage unit  150  also stores programs executed by calculation unit  120  and evaluation unit  130 , image data including the sentence to be uttered by evaluatee U, and image data to be used in outputting the result of the evaluation on the cognitive function of evaluatee U and indicating the result of the evaluation. 
     Instruction unit  160  is a control device that causes output unit  140  to output the sentence data indicating the sentence to be uttered by evaluatee U. Instruction unit  160  is communicatively coupled to cognitive function evaluation device  100 , for example. The instruction unit obtains an instruction indicating start of evaluation on the cognitive function of evaluatee U from a user interface (not shown), such as a touch panel or buttons, operated by a user (e.g., evaluatee U or an assistant of evaluatee U) of cognitive function evaluation device  100 . Upon receipt of the instruction, the instruction unit causes output unit  140  to output image data (e.g., image  410  shown in (a) of  FIG. 4 ) stored in storage unit  150  prepared in advance to instruct evaluatee U to utter a certain sentence. Specifically, instruction unit  160  is a processor, a microcomputer, or a dedicated circuit. Note that calculation unit  120 , evaluation unit  130 , and instruction unit  160  may be integrated in a single processor, a microcomputer, or a dedicated circuit with corresponding functions, or may be achieved by a combination of two or more of processors, microcomputers, and dedicated circuits. Control programs executed by instruction unit  160  may be stored in storage unit  150 . 
     Processing Procedure of Cognitive Function Evaluation Method 
     Now, a specific processing procedure of a cognitive function evaluation method executed by cognitive function evaluation device  100  will be described. 
     First, output unit  140  outputs sentence data stored in storage unit  150  and indicating the sentence to be uttered by evaluatee U to display device  400  (step S 101 ). Specifically, in step S 101 , instruction unit  160  causes output unit  140  to output sentence data stored in storage unit  150  and indicating a certain sentence that instructs evaluatee U to utter a sentence. Display device  400  displays an image indicated by the sentence data obtained from output unit  140 . 
     Next, obtainment unit  110  obtains utterance data on evaluatee U via voice collection device  300  (step S 102 ). In step S 102 , for example, evaluatee U utters a sentence such as “Kita kara kita kata tatakiki” displayed on display device  400 . Obtainment unit  110  obtains, as the utterance data, the voice of evaluatee U uttering the sentence “Kita kara kita kata tatakiki” via voice collection device  300 . 
     After that, calculation unit  120  calculates the feature based on the utterance data obtained in step S 102  (step S 103 ). In step S 103 , for example, calculation unit  120  extracts “ta” uttered first in “Kita kara kita kata tatakiki” contained in the utterance data and calculates, as the feature, the difference in the voice pressure between the consonant and the subsequent vowel of the extracted “ta”. 
     As described above, the feature calculated by calculation unit  120  is not limited thereto. Specific examples of the feature calculated by calculation unit  120  which will be described later. 
     Next, evaluation unit  130  evaluates the cognitive function of evaluatee U from the feature calculated by calculation unit  120  in step S 103  (step S 104 ). In step S 104 , evaluation unit  130  evaluates the cognitive function of evaluatee U from, for example, the feature calculated by calculation unit  120  in step S 103  and reference data  151  stored in storage unit  150 . 
     After that, output unit  140  outputs the result of the evaluation on the cognitive function of evaluatee U by evaluation unit  130  (step S 105 ). In step S 105 , output unit  140  obtains, for example, an image corresponding to the result of the evaluation by evaluation unit  130  in step S 104  from storage unit  150  and sends the obtained image to display device  400 . 
     Display device  400  obtains the image output from output unit  140  and displays the image. Accordingly, evaluatee U easily knows the result of the evaluation on the cognitive function. 
       FIG. 4  shows an example method of obtaining the voice data on evaluatee U using obtainment unit  110  in step S 101  shown in  FIG. 3 . Specifically, (a) of  FIG. 4  shows an example of image  410  displayed on display device  400  before cognitive function evaluation device  100  causes evaluatee U to utter a sentence. In  FIG. 4 , (b) shows an example of image  420  displayed on display device  400  when cognitive function evaluation device  100  obtains the utterance data on evaluatee U. In  FIG. 4 , (c) shows evaluatee U uttering the sentence displayed on display device  400 . 
     As shown in (a) of  FIG. 4 , cognitive function evaluation device  100  causes display device  400  to display image  410  containing a sentence “Come closer to the round screen in front of the microphone and speak” for evaluatee U before obtaining the utterance data from evaluatee U. The content of the sentence shown in (a) of  FIG. 4  may be, for example, explained directly to evaluatee U by a doctor or any other practitioner or may be listened as recorded voice to by evaluatee U. 
     Next, as shown in (b) of  FIG. 4 , display device  400  displays image  420  containing a sentence to be uttered by evaluatee U. As shown in (b) of  FIG. 4 , image  420  is displayed which contains the sentence of “Kita kara kita kata tatakiki”, for example. 
     After that, as shown in (c) of  FIG. 4 , evaluatee U utters the sentence contained in image  420 . In the example shown in (c) of  FIG. 4 , calculation unit  120  extracts, for example, “ta” uttered first in the sentence “Kita kara kita kata tatakiki”. Calculation unit  120  calculates the voice pressures of “t” as the consonant and “a” as the subsequent vowel in the extracted “ta”, and calculates, as the feature, the difference in the voice pressure between “t” and “a” from the calculated voice pressures. 
     In this manner, the sentence to be uttered by evaluatee U is displayed as image  420  not to cause a noise when detecting the voice of evaluatee U, as compared to the case where a doctor or any other practitioner tells the contents of the sentence to evaluatee U. 
     Details of the Feature 
     Now, details of the feature used when cognitive function evaluation device  100  evaluates the cognitive function level of evaluatee U will be described. 
       FIG. 5  shows example utterance data indicating the voice of evaluatee U. Specifically,  FIG. 5  is a graph showing utterance data when evaluatee U utters the sentence “Kita kara kita kata tatakiki”. In the graph shown in  FIG. 5 , the horizontal axis represents the time, whereas the vertical axis represents power (i.e., the voice pressure). Decibel (dB) is used as the unit of the power represented by the vertical axis of the graph of  FIG. 5 . 
     In the graph shown in  FIG. 5 , variations in the voice pressure are found which correspond to “ki”, “ta”, “ka”, “ra”, “ki”, “ta”, “ka”, “ta”, “ta”, “ta”, “ki”, and “ki”. In step S 102  shown in  FIG. 3 , obtainment unit  110  obtains, as the utterance data from evaluatee U, the data shown in  FIG. 5 . For example, in step S 103  shown in  FIG. 3 , calculation unit  120  calculates the voice pressures of “t” and “a” of “ta”, which appears first, from the utterance data shown in  FIG. 5  by a known method. Calculation unit  120  calculates, as the feature, difference ΔP 1  in the voice pressure between “t” and “a” from the calculated voice pressures of “t” and “a”. In this case, reference data  151  contains a threshold that is difference ΔP 1  in the voice pressure. For example, evaluation unit  130  determines that evaluatee U has dementia, if the difference is larger than or equal to the threshold; and evaluates the evaluatee as a healthy person, if the difference is smaller than the threshold. 
     Note that the feature may be a variation in the standard deviation among a plurality of differences in the voice pressure. In this case, calculation unit  120  calculates, for example, differences ΔP 1  to ΔP 9  in the voice pressure shown in  FIG. 5  and calculates, as the feature, the standard deviation among differences ΔP 1  to ΔP 9  in the voice pressure. As compared to healthy people, dementia patients have a large standard deviation among the differences in the voice pressure. If the standard deviation among the differences in the voice pressure is employed as the feature, reference data  151  contains a threshold that is the value of the standard deviation. For example, evaluation unit  130  determines that evaluatee U has dementia, if the value is greater than or equal to the threshold; and evaluates the evaluatee as a healthy person, for example, if the value is smaller than the threshold. The number of the differences in the voice pressure used in calculating the standard deviation is not particularly limited and may be two, three, or more. 
       FIG. 6  illustrates formant frequencies calculated from the utterance data by calculation unit  120 . In the graph shown in  FIG. 6 , the horizontal axis represents the frequency [Hz], whereas the vertical axis represents the amplitude. 
     As indicated by the broken lines in  FIG. 6 , a plurality of peaks are found in the data obtained by converting the horizontal axis of the utterance data into the frequency. Out of the peaks, first formant frequency F 1  has the lowest peak. Second formant frequency F 2  has the second lowest peak next to first formant frequency F 1 . Third formant frequency F 3  has the third lowest peak next to second formant frequency F 2 . In this manner, calculation unit  120  extracts a vowel from the utterance data, which has been obtained by obtainment unit  110 , by a known method. The calculation unit then convers the utterance data on the extracted vowel into the amplitude of the frequency to calculate the spectrum of the vowel, thereby calculating the formant. 
     According to the graph shown in  FIG. 6 , the calculation is made by converting the utterance data obtained from evaluatee U into data on the amplitude of the frequency and then obtaining the envelope of the data. The envelope may be calculated by cepstral analysis or linear predictive coding (LPC), for example. 
       FIG. 7  shows example time changes in formant frequencies calculated from the voice data by calculation unit  120 . Specifically,  FIG. 7  is a graph for illustrating example time changes in first formant frequency F 1 , second formant frequency F 2 , and third formant frequency F 3 . 
     For example, cognitive function evaluation device  100  causes evaluatee U to utter syllables including consecutive vowels such as “aiueo”. Specifically, output unit  140  outputs, to display device  400 , sentence data indicating a sentence containing a character string of the syllables including the consecutive vowels such as “aiueo” to cause display device  400  to display the sentence. This causes evaluatee U to utter the syllables including the consecutive vowels such as “aiueo”. Calculation unit  120  calculates first formant frequency F 1  and second formant frequency F 2  of each vowel from the utterance data indicating the voice of evaluatee U. In addition, calculation unit  120  calculates, as the feature, at least one of the amounts of changes in first formant frequency F 1  and second formant frequency F 2  of each consecutive vowel of the character string, the times required for the changes in first formant frequency F 1  and second formant frequency F 2  of each consecutive vowel of the character string, or the rates of the changes that are the ratios of the amounts of changes to the required times. 
     As compared to healthy people, dementia patients have large amounts, long times, and high rates of changes in first formant frequency F 1  and second formant frequency F 2 . If one of the amounts of changes, the required times, or the rates of changes is employed as the feature, reference data  151  contains a threshold that is the value of one of the amounts of changes, the required times, or the rates of changes. For example, evaluation unit  130  determines that evaluatee U has dementia, if the value is greater than or equal to the threshold; and evaluates the evaluatee as a healthy person, for example, if the value is smaller than the threshold. 
     The vowels contained in the sentence uttered by evaluatee U may not be consecutive. Specifically, output unit  140  may output, to display device  400 , sentence data indicating a sentence containing a character string of inconsecutive vowels, such as “i” and “u” of “taiyou” to cause display device  400  to display the sentence. In this case, calculation unit  120  may calculate, as the feature, a variation in at least one of first formant frequency F 1  of each vowel, second formant frequency F 2  of each vowel, or the ratio of second formant frequency vowel F 2  of each vowel to first formant frequency F 1  of the vowel. The degree of variation calculated as the feature is, for example, a standard deviation. As compared to healthy people, dementia patients have a larger variation. If the variation (specifically, the standard deviation) is employed as the feature, reference data  151  contains a threshold that is the value of the standard deviation. For example, evaluation unit  130  determines that evaluatee U has dementia, if the value is greater than or equal to the threshold, evaluates the evaluatee as a healthy person, for example, if the value is smaller than the threshold. 
     A sentence such as “Kita kara kita kata tatakiki” contains no syllables consisting of a vowel only but open syllables, each of which consists of a consonant and a subsequent vowel. In this case, calculation unit  120  may extract, for example, the phoneme of a subsequent vowel and calculate the formant frequency of the subsequent vowel to calculate the amounts of a change, the required time, or the rate of change in the formant frequency. Each character string of the consecutive vowels may be a character string of a subsequent vowel and a vowel following the subsequent vowel. 
     Note that cognitive function evaluation device  100  may include a time measurement unit such as a real time clock (RTC) to measure the time. 
       FIG. 8  illustrates an example feature of utterance data to be evaluated by evaluation unit  130 . In the graph shown in  FIG. 8 , the horizontal axis represents first formant frequency F 1 , whereas the vertical axis represents second formant frequency F 2 . 
     As shown in  FIG. 8 , calculation unit  120  calculates first formant frequency F 1  and second formant frequency F 2  of a single vowel and plots the result at a single point in the graph shown in  FIG. 8 . For example, evaluatee U utters “a”, “i”, “u”, “e”, “o”. Specifically, output unit  140  outputs, to display device  400 , the sentence data indicating the sentence containing “a”, “i”, “u”, “e”, and “o” to cause display device  400  to display the sentence. This causes evaluatee U to utter “a”, “i”, “u”, “e”, “o”. Calculation unit  120  calculates first formant frequency F 1  and second formant frequency F 2  of each of “a”, “i”, “u”, “e”, and “o” contained in the utterance data. Accordingly, calculation unit  120  plots the point corresponding to each of “a”, “i”, “u”, “e”, and “o” in the coordinate space shown in  FIG. 8 . In this manner, calculation unit  120  calculates, as the feature, at least one of the shape or the area of the polygon defined by plotting the ratio of second formant frequency F 2  to first formant frequency F 1  calculated from each of the at least three vowels in the coordinate space. The coordinate space (specifically, the coordinate space shown in  FIG. 8 ) is defined by second formant frequency F 2  of the vowel with respect to first formant frequency F 1  of the vowel. 
     As compared to healthy people, dementia patients have the polygon defined in this manner with a small area. If the area of the polygon is employed as the feature, reference data  151  contains a threshold that is the value of the area of the polygon. For example, evaluation unit  130  determines that evaluatee U is a healthy person, if the value is greater than or equal to the threshold; and evaluates the evaluatee as having dementia, for example, if the value is smaller than the threshold. 
     As compared to healthy people, dementia patients have the polygon defined in this manner in a shape with the points close to each other. Assume that the polygon is a pentagon and the shape of the polygon is approximated to a regular pentagon. As compared to healthy people, dementia patients have a polygon in a shape largely deviated from the regular pentagon. If the shape of the polygon is employed as the feature, reference data  151  contains a threshold that is the value of the distance between the points constituting the polygon, or the amount of deviation of each point when the pentagon is approximated to a regular pentagon. For example, evaluation unit  130  determines that evaluatee U is a healthy person, if the value is greater than or equal to the threshold; and evaluates the evaluatee as having dementia, for example, if the value is smaller than the threshold. 
     The vowels employed for plotting may be subsequent vowels subsequent to consonants. In a language other than Japanese, any other element such as “A”, a phonetic symbol, may be used in addition to or in place of the vowels “a”, “i”, “u”, “e”, and “o” in the Japanese language. 
     The number of plotted points may be three or more, as long as at least one of the shape or the area of the polygon defined by the points can be calculated. 
       FIG. 9  illustrates another example feature of utterance data to be evaluated by evaluation unit  130 . 
     As shown in  FIG. 9 , calculation unit  120  calculates first formant frequency F 1  and second formant frequency F 2  of a single vowel and plots the result at a single point in the graph shown in  FIG. 9 . For example, cognitive function evaluation device  100  causes evaluatee U to utter syllables including consecutive vowels such as “ai”, “au”, and “oi”. Specifically, output unit  140  outputs, to display device  400 , sentence data indicating the sentence containing a character string of the syllables including the consecutive vowels such as “ai”, “au”, and “oi” to cause display device  400  to display the sentence. This causes evaluatee U to utter, “ai”, “au”, and “oi”, for example. Calculation unit  120  calculates first formant frequency F 1  and second formant frequency F 2  of each of “a” and “i” of “ai”, for example, contained in the utterance data. Accordingly, calculation unit  120  plots the points corresponding to “a” and “i”. In this manner, the sentence indicated by the sentence data output from output unit  140  may contain at least two consecutive vowels. Calculation unit  120  may calculate, as the feature, the positional relationship when plotting the ratio of second formant frequency F 2  to first formant frequency F 1  of the vowel calculated from each of the at least two respective vowels in the coordinate space (specifically, the coordinate space shown in  FIG. 9 ) defined by second formant frequency F 2  of the vowel with respect to first formant frequency F 1  of the vowel. 
     As compared to healthy people, dementia patients have the points plotted in this manner at a small distance. If the positional relationship among the points is employed as the feature, reference data  151  contains a threshold that is the distance between the points. For example, evaluation unit  130  determines that evaluatee U is a healthy person, if the value is greater than or equal to the threshold; and evaluates the evaluatee as having dementia, for example, if the value is smaller than the threshold. 
     Whether a person has dementia is determined by the MoCA test, which is a batch test for examination of dementia, taken by the person. 
       FIG. 10  shows scores acquired by people in a MoCA test. 
     The present inventors gathered evaluatees including healthy people with normal controls (NC), mild dementia patients with mild cognitive impairment (MCI), and dementia patients with AD to conduct the MoCA test. The number of evaluatees (i.e., the number of subjects) with NC was 90, the number of evaluatees with MCI was 94, and the number of evaluatees with AD was 93. 
     It is found from  FIG. 10  that the average scores in the MoCA test (i.e., the average MoCA scores) and the score ranges in the MoCA test (i.e., the MoCA score ranges) are different among the NC, MCI, and AD groups. Specifically, the average score of the NC group in the MoCA was 27.4, the average score of the MCI group in the MoCA was 22.1, and the average score of the AD group in the MoCA was 16.2. 
     The features, which are described above and based on the voice data (i.e., the utterance data) indicating the voices of the people, are calculated from the people who have taken the MoCA test. From the calculation, reference data  151  is prepared, which indicates the relationship between the features of the people and is based on the voice data and the cognitive functions of the people. For example, if evaluation unit  130  determines that evaluatee U has NC, MCI, or AD, reference data  151  corresponds to two thresholds (e.g., a first threshold and a second threshold) with different values as the threshold of the feature described above. For example, evaluation unit  130  evaluates evaluatee U as having NC, if the feature calculated from the utterance data obtained from evaluatee U is smaller than the first threshold. The evaluation unit evaluates evaluatee U as having MCI, if the feature is greater than or equal to the first threshold and smaller than the second threshold. The evaluation unit evaluates evaluatee U as having AD, if the feature is greater than or equal to the second threshold. Cognitive function evaluation device  100  uses reference data  151  to simply evaluate the cognitive function of evaluatee U from the feature based on utterance data on evaluatee U and reference data  151 . Note that one, two, or more threshold(s) of the feature may be used as reference data  151 . 
       FIG. 11  shows an example image displayed on display device  400  and indicating a decline in the cognitive function. 
     Display device  400  displays, as the result of the evaluation by evaluation unit  130 , image  430  as shown in  FIG. 11 . Image  430  is an example image displayed if evaluation unit  130  evaluates the cognitive function of evaluatee U as having AD, for example. In this manner, display device  400  then displays, as an image, the result of the evaluation by evaluation unit  130 . With this configuration, for example, if evaluatee U evaluates the cognitive function using cognitive function evaluation device  100  at home or any other place, cognitive function evaluation device  100  may encourage evaluatee U to see a doctor or any other practitioner. 
     Like “Kita kara kita kata tatakiki” shown in (c) of  FIG. 4 , the sentence may be uttered by evaluatee U not only once but also a plurality of times. 
       FIG. 12  shows another example image displayed on display device  400  in obtaining the utterance data on evaluatee U. 
     Like image  440  shown in  FIG. 12 , the sentence data containing a sentence to be uttered by evaluatee U as instructed by output unit  140  may contain an explanation that causes the evaluatee to utter the sentence a plurality of times. For example, calculation unit  120  calculates, as the feature, the time required for evaluatee U to utter the sentence. As compared to healthy people, dementia patients need a long time to read a sentence. In this case, reference data  151  contains a threshold that is the reading time. For example, evaluation unit  130  determines that evaluatee U has dementia, if the value is greater than or equal to the threshold, and evaluates the evaluatee as a healthy person, for example, if the value is smaller than the threshold. 
     Evaluatee U here may utter a single sentence a plurality of times. Specifically, output unit  140  may further output an instruction that causes evaluatee U to utter the sentence the plurality of times. Calculation unit  120  may calculate, as the feature, the amount of change in the reading time calculated from reading times obtained for the plurality of times evaluatee U uttered the sentence. 
     For example, if the time required for evaluatee U to utter the sentence for the first time is ten seconds and the time required for evaluatee U to utter the sentence for the second time is eight seconds, the amount of change is two seconds. Alternatively, assume that evaluatee U utters a sentence three or more times. In this case, for example, calculation unit  120  may calculate, as the feature, the standard deviation of the time required for evaluatee U to utter the sentence each time, or the average of the amount of changes calculated the plurality of times. 
     As compared to healthy people, dementia patients have a large amount of change in the reading time for the sentence. In this case, reference data  151  contains the amount of change in the reading time. For example, evaluation unit  130  determines that evaluatee U has dementia, if the value is greater than or equal to the threshold, and evaluates the evaluatee as a healthy person, for example, if the value is smaller than the threshold. 
     While  FIG. 12  illustrates an instruction that causes evaluatee U to utter the sentence three times, the sentence may be uttered two, four, or more times. 
     The sentence data containing the sentence to be uttered by evaluatee U as instructed by output unit  140  may contain an explanation that causes the evaluatee to utter a sentence a plurality of times or to utter a plurality of sentences. 
     Advantages 
     As described above, cognitive function evaluation device  100  according to the embodiment includes obtainment unit  110 , calculation unit  120 , evaluation unit  130 , and output unit  140 . Obtainment unit  110  obtains utterance data indicating the voice of evaluatee U uttering a sentence as instructed. Calculation unit  120  calculates, from the utterance data obtained by obtainment unit  110 , a feature based on the utterance data. Evaluation unit  130  compares the feature calculated by calculation unit  120  to reference data  151  indicating a relationship between voice data indicating voices of people and cognitive functions of the people to evaluate the cognitive function of the evaluatee. Output unit  140  outputs the sentence to be uttered by evaluatee U and output a result of the evaluation by evaluation unit  130 . 
     With this configuration, cognitive function evaluation device  100  obtains, from evaluatee U, the voice data from which the cognitive function is accurately evaluated by evaluation unit  130 . Accordingly, cognitive function evaluation device  100  simply and accurately evaluates the cognitive function of evaluatee U. 
     For example, the sentence to be uttered by evaluatee U may contain a character string of at least one of consecutive syllables, each of which consists of a consonant and a vowel subsequent to the consonant, or consecutive syllables, each of which consists of a vowel only. 
     That is, the voice data evaluated by evaluation unit  130  may contain at least one of consecutive syllables, each of which consists of a consonant and a vowel subsequent to the consonant, or consecutive syllables, each of which consists of a vowel only. For example, it is found from  FIG. 5  that the vowel “a” has a higher voice pressure than the consonant “t”. In this manner, for example, at voice collection, voice collection device  300  easily collects vowels as compared to consonants. The evaluatee repeatedly utters at least one of a combination of a consonant and a vowel subsequent to the consonant, or a combination of vowels only. This allows analysis of the plurality of vowels, which leads to more accurate evaluation on the cognitive function of evaluatee U. 
     For example, the sentence to be uttered by evaluatee U may contain at least one of character strings of “Kitakaze to taiyo ga deteimasu”, “Tankenka wa bouken ga daisuki desu”, or “Kita kara kita kata tatakiki”. Like these, the sentence indicated by the sentence data output from output unit  140  may contain five or more character strings, each of which includes a stop consonant and a vowel subsequent to the stop consonant. There tends to be a difference in the stop consonant between the voice data on the patients with AD and the people with NC. The sentence to be uttered by evaluatee U may thus be, for example, “Kitakaze to taiyo ga deteimasu”, “Tankenka wa bouken ga daisuki desu”, and “Kita kara kita kata tatakiki”. This allows more accurate evaluation of the cognitive function of evaluatee U. 
     For example, the sentence to be uttered by evaluatee U may contain a character string of consecutive syllables, each of which includes a vowel. Calculation unit  120  may calculate, as the feature, at least one of the amounts of changes in first formant frequency F 1  and second formant frequency F 2  of the vowel, times required for the changes in first formant frequency F 1  and second formant frequency F 2  of the vowel, or the rates of changes that are the ratios of the amount of changes to the required times. 
     First formant frequency F 1  is a peak frequency of an amplitude that can be seen first, counting from the lowest frequency of the human voice. It is known that the first formant frequency tends to reflect the feature related to the movement of the tongue. As compared to the people with NC, the patients with AD often fail to move their tongue well. It is thus considered that there tends to be a difference in first formant frequency F 1  between the people with NC and the patients with AD. For example, the patients with AD often suffer from a decline in the motor function maintaining the position of the tongue or the chin. It is thus considered that the patients with AD thus utter an unstable voice as compared to the people with NC. It is thus considered that, since there are fluctuations in the voices of the patients with AD as compared to the people with NC, the time change in each of first formant frequency F 1  and second formant frequency F 2  tends to be large. In this point of view, one of the amounts of changes in first formant frequency F 1  and second formant frequency F 2 , the required times, and the rates of changes that are the ratios of the amounts of changes to the required times is used as the feature to evaluate the cognitive function. This allows more accurate evaluation on the cognitive function of evaluatee U. 
     For example, the sentence to be uttered by evaluatee U may contain a plurality of syllables, each of which includes a vowel. Calculation unit  120  may calculate, as the feature, at least one of variations in first formant frequency F 1  of the vowel, second formant frequency F 2  of the vowel, or the ratio of second formant frequency F 2  of the vowel to first formant frequency F 1  of the vowel. 
     As described above, as compared to the people with NC, the patients with AD tend to utter a fluctuating voice, and thus first formant frequency F 1  and second formant frequency F 2  tend to vary. It is considered that there is an individual difference in the formant frequency. It is also considered that, there is a correlation between first formant frequency F 1  and second formant frequency F 2 , which also depends on the individual difference. In this point of view, the variation in the ratio of second formant frequency F 2  of the vowel to first formant frequency F 1  of the vowel is used as the feature. This allows more accurate evaluation of the cognitive function of evaluatee U. 
     For example, the sentence to be uttered by evaluatee U may contain at least three syllables, each of which includes a vowel different from the vowels of the other syllables. Calculation unit  120  may calculate, as the feature, at least one of the shape or the area of the polygon defined by plotting the ratio of second formant frequency F 2  to first formant frequency F 1  calculated from the vowel of each of the at least three syllables in the coordinate space defined by second formant frequency F 2  of the vowel with respect to first formant frequency F 1  of the vowel. 
     As described above, as compared to healthy people, dementia patients have the polygon defined in this manner with a small area. As compared to healthy people, dementia patients have the polygon defined in this manner in a shape with the points close to each other. Assume that the polygon is a pentagon and the shape of the polygon is approximated to a regular pentagon. As compared to healthy people, dementia patients have a polygon in a shape largely deviated from the regular pentagon. In this point of view, at least one of the shape or the area of the polygon is employed as the feature, which allows more accurate evaluation on the cognitive function of evaluatee U. 
     For example, the sentence to be uttered by evaluatee U may contain at least two consecutive syllables, each of which includes a vowel different from the vowel of the other syllable. Calculation unit  120  may calculate, as the feature, the positional relationship when plotting the ratio of second formant frequency F 2  to first formant frequency F 1  calculated from the vowel of each of the at least two consecutive syllables in the coordinate space defined by second formant frequency F 2  of the vowel with respect to first formant frequency F 1  of the vowel. 
     As described above, as compared to healthy people, dementia patients have the points plotted in this manner at a small distance. In this point of view, the positional relationship between the points is employed as the feature, which allows more accurate evaluation on the cognitive function of evaluatee U. 
     For example, the sentence to be uttered by evaluatee U may contain a syllable including a consonant and a vowel subsequent to the consonant. Calculation unit  120  may calculate, as the feature, the difference in the voice pressure between the consonant and the vowel. 
     For example, evaluation unit  130  determines that evaluatee U has dementia, if the value is greater than or equal to the threshold, and evaluates the evaluatee as a healthy person, for example, if the value is smaller than the threshold. For example, the feature may be a variation in the standard deviation among a plurality of differences in the voice pressure. In this case, calculation unit  120  calculates differences ΔP 1  to ΔP 9  in the voice pressure shown in  FIG. 5 , for example, and calculates, as the feature, the standard deviation among difference ΔP 1  to ΔP 9  in the voice pressure. As compared to healthy people, dementia patients have a large standard deviation among the differences in the voice pressure. In this point of view, the difference in the voice pressure is employed as the feature, which allows more accurate evaluation on the cognitive function of evaluatee U. 
     For example, calculation unit  120  may calculate, as the feature, a time required for evaluatee U to utter the sentence. 
     As described above, as compared to healthy people, dementia patients need a long time to read a sentence. In this point of view, the reading time for the sentence is employed as the feature, which allows more accurate evaluation on the cognitive function of evaluatee U. 
     For example, output unit  140  may further output an instruction for causing evaluatee U to utter the sentence a plurality of times. Calculation unit  120  may calculate, as the feature, the amount of change in the reading time calculated when the evaluatee utters the sentence. 
     As described above, as compared to healthy people, dementia patients have a large amount of change in the reading time for a sentence. In this point of view, the amount of change in the reading time for the sentence is employed as the feature. This allows more accurate evaluation on the cognitive function of evaluatee U. 
     For example, cognitive function evaluation device  100  may include storage unit  150  that stores reference data  151 . 
     That is, cognitive function evaluation device  100  may communicate with an external server device or any other device that stores reference data  151  to evaluate the cognitive function of evaluatee U. Alternatively, the device may include storage unit  150  being a storage device that stores reference data  151 . With this configuration, cognitive function evaluation device  100  evaluates the cognitive function of evaluatee U without being connected to a network for communications with an external server device. This improves the convenience of cognitive function evaluation device  100 . 
     Cognitive function evaluation system  200  according to the embodiment includes cognitive function evaluation device  100 , voice collection device  300 , and display device  400 . Voice collection device  300  detects the voice of evaluatee U. Display device  400  displays the sentence and the result of the evaluation output from output unit  140 . 
     With this configuration, cognitive function evaluation system  200  displays the sentence to be uttered by evaluatee U using display device  400 , detects the voice of evaluatee U using voice collection device  300 , evaluates the cognitive function of evaluatee U using cognitive function evaluation device  100 , and displays the result of the evaluation using display device  400 . That is, cognitive function evaluation system  200  obtains, from evaluatee U, voice data from which the cognitive function is accurately evaluated by evaluation unit  130 . Accordingly, cognitive function evaluation device  200  simply and accurately evaluates the cognitive function of evaluatee U. 
     A cognitive function evaluation method according to the embodiment is executed by a computer (specifically, cognitive function evaluation device  100 ). The cognitive function evaluation method includes: outputting a sentence to be uttered by evaluatee U; obtaining utterance data indicating the voice of evaluatee U; calculating, from the utterance data obtained in the obtaining, a feature based on the utterance data; evaluating the cognitive function of evaluatee U by comparing the feature calculated in the calculating to reference data indicating a relationship between voice data indicating the voices of people and the cognitive functions of the people; and outputting the result of the evaluating. 
     With this feature, the cognitive function evaluation method according to the present disclosure allows obtainment of voice data from which the cognitive function is accurately evaluated, from evaluatee U. Accordingly, the cognitive function evaluation method according to the present disclosure allows simple and accurate evaluation on the cognitive function of evaluatee U. 
     The present disclosure may be implemented as a non-transitory computer-readable storage medium storing a program that causes a computer to execute the steps included in the cognitive function evaluation method. 
     Variations 
     Now, cognitive function evaluation systems according to Variation 1 and Variation 2 of the embodiment will be described. Note that substantially the same constituent elements are assigned with the same reference marks, and redundant descriptions may be omitted or simplified. 
       FIG. 13  shows a configuration of the cognitive function evaluation system according to Variation 1 of the embodiment. 
     Like cognitive function evaluation system  200  according to the embodiment, cognitive function evaluation system  200   a  according to Variation 1 of the embodiment includes cognitive function evaluation device  100 , voice collection device  300 , and display device  400 . Cognitive function evaluation system  200   a  may include pop guard  320  to cover voice collection device  300 , for example. 
     Cognitive function evaluation system  200   a  employs directional voice collection device  300 . Voice collection device  300  and display device  400  are here arranged such that the direction in which voice collection device  300  exhibits the maximum sensitivity (i.e., voice collection direction V 2  shown in  FIG. 13 ) agrees with normal direction V 1  of display surface  401  on which display device  400  displays question information. Specifically, voice collection device  300  and display device  400  are arranged on a fixed object such as a desk such that normal direction V 1  is parallel to voice collection direction V 2 . Note that voice collection device  300  and display device  400  may be fixed to a building material, for example. Cognitive function evaluation system  200   a  may include a fixture to establish a fixed positional relationship between voice collection device  300  and display device  400 . 
     With this configuration, voice collection direction V 2  tends to agree with the direction into which evaluatee U speaks even while viewing display device  400 . The positional relationship as in cognitive function evaluation system  200   a  causes voice collection device  300  to accurately detect the voice of evaluatee U. 
     Now, a cognitive function evaluation system according to Variation 2 of the embodiment will be described. 
       FIG. 14  shows a configuration of the cognitive function evaluation system according to Variation 2 of the embodiment. 
     Like cognitive function evaluation system  200  according to the embodiment, cognitive function evaluation system  200   b  according to Variation 2 of the embodiment includes cognitive function evaluation device  100 , voice collection device  300   a , and display device  400 . 
     Like voice collection device  300 , voice collection device  300   a  is a microphone that detects the voice of evaluatee U and outputs voice data indicating the detected voice to cognitive function evaluation device  100 . Like voice collection device  300  in cognitive function evaluation system  200   a  according to Variation 1 of the embodiment, voice collection device  300   a  is directional. 
     In cognitive function evaluation system  200   b , voice collection device  300   a  and display device  400  are formed integrally. Specifically, voice collection device  300   a  and display device  400  are arranged in a housing. In the manufacturing process, voice collection device  300   a  and display device  400  may be integrally formed such that normal direction V 1  agrees with voice collection direction V 2 . This may reduce the deviation between normal direction V 1  and voice collection direction V 2  when evaluatee U utilizes cognitive function evaluation system  200   b.    
     Other Embodiments 
     The cognitive function evaluation devices or other elements have been described above in the embodiment and Variations 1 and 2 of the embodiment. The present disclosure is not limited to the embodiment and variations. 
     In the embodiment described above, Alzheimer&#39;s disease is named as a specific example of a decline in the cognitive function. The “cognitive function” represents, however, capabilities such as recognition, remembering, or judgment, and the “dementia” represents the symptoms of decreased cognitive function as described above. That is, the cognitive function evaluation device evaluates the cognitive function levels not only in Alzheimer&#39;s disease but also in vascular dementia or drunkenness, for example. 
     In the embodiment described above, in order to evaluate the cognitive function level of evaluatee U, the data indicating the relationship between the scores in the MoCA test and the features based on the formants is, as reference data  151 , stored in advance in storage unit  150 . However, the reference data may be any data as long as being compared to the features of the formants to allow evaluation on the cognitive function level. The reference data is not limited to the data indicating the relationship between the scores in the MoCA test and the features of the formants. For example, the reference data may be data indicating the relationship between scores in a mini-mental state examination (MMSE), for example, and the features of formants. 
     The embodiment described above includes the expressions such as “greater than or equal to the threshold” and “smaller than the threshold”, which are not used in a strict sense. For example, the expression “greater than or equal to the threshold” may simply mean “greater than the threshold”. The comparative expressions such as “greater than or equal to the threshold” and “smaller than the threshold” mean that distinction is made using the threshold as the boundary, and may also mean “greater than the threshold” and “smaller than or equal to the threshold”, respectively. 
     The relationship between the utterance data and the degree of dementia in the reference data described above is based on data analysis of the evaluatees gathered by the present inventors at present. In the future, data analysis may be performed with more evaluatees or under modified conditions, which may change the evaluation standard. In the embodiment described above, the difference in the voice pressure is employed as the feature. For example, the evaluation unit determines that evaluatee U has dementia, if the value is greater than or equal to the threshold; and evaluates the evaluatee as a healthy person, for example, if the value is smaller than the threshold. The evaluation standard is not limited thereto. In this case, for example, the evaluation unit may determine that evaluatee U has dementia, if the value is smaller than the threshold; and evaluates the evaluatee as a healthy person, for example, if the value is greater than or equal to the threshold. It also applies to how to treat any other threshold as the feature. 
     In the embodiment described above, only the utterance data obtained from the evaluatee is calculated as the feature to evaluates the cognitive function of the evaluatee. The evaluation may be however performed by combining data sets that allow evaluation on other known cognitive functions. For example, it is known that there is a correlation between a cognitive function and walking data such as a step length, a step width, or a walking speed. A combination of the utterance data on the evaluatee evaluated in the embodiment described above and the walking data on the evaluatee may be used for the evaluation on the cognitive function, which leads to more accurate evaluation on the cognitive function of the evaluatee. 
     The present disclosure may be implemented not only by the cognitive function evaluation device and the cognitive function evaluation system, but by a program containing, as steps, the processing performed by the constituent elements of the cognitive function evaluation device and the cognitive function evaluation system. The present disclosure may also be implemented by a computer-readable recording medium storing the program, for example, a recording medium such as a flexible disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, a DVD-RAM, a Blu-ray Disk (registered trademark, BD), or a semiconductor memory. The program may be distributed via a communication channel such as internet. 
     The general and specific aspects described above may be implemented by a system, a device, an integrated circuit, a computer program, or a computer-readable recording medium, or any combination of systems, device, integrated circuits, computer programs, and computer-readable recording media. For example, the constituent elements of the cognitive function evaluation device are not necessarily included in a housing, buy may be arranged in different places and connected with various data transfer available. 
     The present disclosure includes other embodiments, such as those obtained by variously modifying the embodiment as conceived by those skilled in the art or those achieved by freely combining the constituent elements and functions in the embodiment without departing from the scope and spirit of the present disclosure.