JP7191269B1 - Device for promoting vocal health, method for promoting vocal health, and program for promoting vocal health - Google Patents

Abstract

An object of the present invention is to promote health by a user himself/herself. A voice diagnostic device (20) is a device for promoting health by training vocalization, and includes a storage unit (21) that stores training sounds (21F) for each vocalization training zone for improving the degree of opening of the soft palate; A conversion unit 24 that converts voice data input by a user into voice frequency data, a determination unit 26 that determines a scale that occupies the smallest octave for the voice frequency data converted by the conversion unit 24, and a determination unit 26. and a training sound 21F corresponding to the vocalization training zone determined by the zone determining unit 30 for determining the vocalization training zone based on the minimum scale determined by the zone determining unit 30, while providing the training sound 21F and a providing unit 22 for causing the user to speak in time with the training, and providing the user with information indicating the effect of the training based on the voice data of the voice. [Selection drawing] Fig. 12

Description

Article 30, Paragraph 2 of the Patent Act applies Bio Magazine on November 9, 2021. Published in the December 2021 issue of the publication anemone, pages 62-68 Published on January 12, 2020 in "How to brush your voice to turn your life around" published by Business Co., Ltd. December 2021 On the 30th, Amazon (https://www.amazon.co.jp/) will sell an e-book of “How to brush your voice to improve your life” Website (https:/) on December 30, 2021 / voice.koeshindan.jp/) Released sound diagnosis software / light Released on December 26, 2021 on YouTube video (https://www.youtube.com/watch?v=ys_xQziCDO4) Released on the website of the Japan Voice Diagnosis Association (https://koeshindan.jp/) on December 30, 1998 Yumiko Nakajima official site (https://yumiko) on November 1, 2021 -nakajima.com/) Vocal pamphlet around June 2021 (https://www.dropbox.com/s/56d70qhmnrb6ooe/%E5%A3%B0%E9%81%93%EF%BE %8A%EF%BE%9F%EF%BE%9D%EF%BE%8C.pdf?dl=0)

TECHNICAL FIELD The present invention relates to a health promotion device, a health promotion method, and a health promotion program.

2. Description of the Related Art Conventionally, a voice diagnostic device for diagnosing voice is known. For example, Patent Literature 1 discloses a voice diagnosis apparatus that converts the voice of a living body into voice frequency data by Fourier transform and diagnoses the psychological state, health condition, or thought pattern of the living body. According to this voice diagnosis device, voice frequency data can be distributed in a distribution chart and displayed on a display unit such as a display, so that it is effective when used as a diagnostic tool by a voice psychologist.

Japanese Patent No. 6029223

However, the conventional voice diagnostic apparatus has a problem that it is difficult for the user to self-diagnose the voice. That is, since many users do not have knowledge about speech psychology, they usually cannot understand the diagnosis correctly without receiving advice from a speech psychologist. In recent years, due to the influence of the new coronavirus infectious disease, there is a need for users to self-diagnose voice at home, etc., and there is a desire for a new device to meet such needs.

Another problem with the conventional voice diagnostic apparatus is that it is difficult for the user to promote his/her own health. In other words, voice diagnosis is a method for promoting health by knowing one's own voice and adjusting one's voice (described later). Since it is difficult for the user to self-diagnose the voice with the conventional voice diagnosis device, it is also difficult for the user to promote his/her own health.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a vocal health promotion apparatus, a vocal health promotion method, and a vocal health promotion program that enable users to promote their own health.

One aspect of the present invention is a vocalization health promotion device that promotes health by training vocalization, wherein for each vocalization training zone for improving the degree of opening of the soft palate, a musical scale corresponding to the vocalization training zone is provided. A storage unit that stores training sounds that are sound data, a conversion unit that converts voice data input by a user into voice frequency data, and a frequency band corresponding to a scale for the voice frequency data converted by the conversion unit a determination unit for determining a scale in which the ratio of the sum of voice powers to the total sum of voice powers in the frequency band corresponding to the octave to which the scale belongs is the minimum, and the minimum determined by the determination unit. a zone determination unit that determines the vocalization training zone based on a scale; and a training sound corresponding to the vocalization training zone determined by the zone determination unit is provided to the user, and a voice is given to the user in accordance with the training sound. and providing information indicating the effect of training to the user based on the audio data of the voice, wherein the zone determination unit associates the scale with the voice color using a circular ring diagram , the voice color corresponding to the minimum scale, or the voice color complementary to the voice color corresponding to the minimum scale, to determine the vocalization training zone.

Another aspect of the present invention is a vocalization health promotion method for promoting health by training vocalization, wherein a computer performs vocalization training for each vocalization training zone for improving the degree of opening of the soft palate. A storage step of storing training sounds, which are sound data of a scale corresponding to a training zone; a conversion step of converting voice data input by a user into voice frequency data; a determining step of determining a scale in which the ratio of the sum of voice powers in the frequency band corresponding to the scale to the total sum of voice powers in the frequency band corresponding to the octave to which the scale belongs is the smallest; a zone determination step of determining the vocalization training zone based on the minimum scale determined in the step of providing the user with a training sound corresponding to the vocalization training zone determined in the zone determination step; and providing information indicating the effect of training to the user based on the voice data of the user, and the zone determination step includes using a ring diagram The scale and voice color are associated, and the vocalization training zone is determined based on the voice color corresponding to the minimum scale or the voice color complementary to the voice color corresponding to the minimum scale.

Another aspect of the present invention is a vocal health promotion program for promoting health by training vocalization, wherein a computer stores the vocalization training zone for improving the degree of opening of the soft palate. A storage step of storing training sounds, which are sound data of a scale corresponding to a training zone; a conversion step of converting voice data input by a user into voice frequency data; a determining step of determining a scale in which the ratio of the sum of voice powers in the frequency band corresponding to the scale to the total sum of voice powers in the frequency band corresponding to the octave to which the scale belongs is the smallest; a zone determination step of determining the vocalization training zone based on the minimum scale determined in the step of providing the user with a training sound corresponding to the vocalization training zone determined in the zone determination step; a provision step of causing the user to vocalize in accordance with the above, and providing information indicating the effect of training to the user based on the audio data of the voice; The scale and voice color are associated, and the vocalization training zone is determined based on the voice color corresponding to the minimum scale or the voice color complementary to the voice color corresponding to the minimum scale.

According to the present invention, it is possible to provide a vocal health promotion device, a vocal health promotion method, and a vocal health promotion program that allow users to promote their own health.

FIG. 4 is an explanatory diagram of voice diagnosis according to the embodiment of the present invention; 1 is a network configuration diagram of a voice diagnosis system in the first embodiment; FIG. 1 is a functional block diagram of a speech diagnosis device according to a first embodiment; FIG. 4 is an internal configuration diagram of a storage unit in the first embodiment; FIG. It is a distribution chart in a 1st embodiment. FIG. 4 is an explanatory diagram of a screen of a user terminal in the first embodiment; FIG. FIG. 4 is an explanatory diagram of a screen of a user terminal in the first embodiment; FIG. FIG. 4 is an explanatory diagram of a screen of a user terminal in the first embodiment; FIG. FIG. 4 is an explanatory diagram of a screen of a user terminal in the first embodiment; FIG. FIG. 4 is an explanatory diagram of a screen of a user terminal in the first embodiment; FIG. 4 is a sequence diagram showing an operation example of the voice diagnosis system in the first embodiment; FIG. FIG. 10 is a functional block diagram of a speech diagnosis device according to a second embodiment; FIG. FIG. 10 is a conceptual diagram of zone data in the second embodiment; It is a schematic diagram which shows the distribution chart in 2nd Embodiment. FIG. 11 is an explanatory diagram of a screen of a user terminal in the second embodiment; FIG. FIG. 11 is a sequence diagram showing an operation example of the voice diagnosis system according to the second embodiment; 1 is a hardware configuration diagram of a voice diagnosis device according to an embodiment of the present invention; FIG.

Embodiments of the present invention will be described below with reference to the drawings. However, the embodiments described below are merely examples. That is, the embodiments described below can be modified in various ways without departing from the spirit of the embodiments.

《What is Voice Diagnosis?》
The present inventor systematized the correlation between voice frequencies and thought patterns as speech psychology based on voice data of more than 20,000 people. By classifying voice frequencies into 12 scales and analyzing what frequencies are included in the voice, it is possible to know the power and mental block of the voice.

FIG. 1 is an explanatory diagram of speech diagnosis according to an embodiment of the present invention, showing the relationship between voice color, musical scale, and thinking. In speech diagnosis, first, human voice frequencies are classified into 12 scales, and the correlation between the scale and the human mind is visualized. Check the pattern of the voice by looking at which color is more or less. Voice patterns are also thought patterns. For example, as shown in FIG. 1, a person who has a lot of magenta M corresponding to scale B (b sound) tends to be rich in receptivity and love (in other words, benevolence). By recognizing thought patterns expressed in colors in this way, one deepens one's self-awareness and raises awareness. By looking at how colors appear in the 8 octave range, you can see the subconscious, conscious, and future consciousness.

<<1st Embodiment>>

[Voice diagnosis system]
FIG. 2 is a network configuration diagram of the voice diagnosis system in the first embodiment. This voice diagnosis system is a system that uses a computer to perform voice diagnosis, and here, a cloud-type voice diagnosis system is exemplified. Specifically, the user terminal 10, the audio diagnostic device 20, and the management terminal 100 are connected via a network such as the Internet network N. FIG.

A user terminal 10 is a computer such as a tablet, a smart phone, or a personal computer operated by a user of this system, and includes a web browser (hereinafter referred to as a browser) for browsing websites. When Web page data is received from the audio diagnosis device 20, the browser analyzes the data and displays (renders) the screen on the display.

Here, it is assumed that the user self-diagnoses the voice at home, and the headset H is connected to the user terminal 10 . The headset H is an input/output device that combines a headphone for listening to voice and a microphone for inputting voice. Such an input/output device may be built in the user terminal 10 instead of the external headset H. FIG.

The voice diagnosis device 20 is a computer that diagnoses the user's voice. For example, when this system is implemented as a web service, the voice diagnostic device 20 is configured with a web server or the like. In FIG. 2 , only the computer main body is depicted as the voice diagnostic device 20 , but peripheral devices such as a display, a keyboard, and a headset H may be connected to the voice diagnostic device 20 .

The management terminal 100 is a computer operated by an administrator of this system. The administrator uses the management terminal 100 to make overall settings for this system.

[Voice diagnosis device]
FIG. 3 is a functional block diagram of the speech diagnosis device 20 according to the first embodiment. The voice diagnosis apparatus 20 is a computer for diagnosing user's voice, and as shown in FIG. A determination unit 26 , a diagnosis unit 27 and an authentication unit 28 are provided. Each of these functional units is implemented in the computer by the CPU executing a program loaded on the memory.

The storage unit 21 is a functional unit that stores various data. For example, the storage unit 21 stores diagnostic history data 21A, diagnostic result data 21B, diagnostic master data 21C, HTML (HyperText Markup Language) data 21D, and the like. In addition, various data necessary for generating web page data, such as CSS (Cascading Style Sheets) data, image data, and program data, are also stored.

The providing unit 22 is a functional unit that provides various data to the user terminal 10 . For example, when a request such as an HTTP (Hypertext Transfer Protocol) request is received from the user terminal 10, Web page data is generated using HTML data 21D or the like and provided to the user terminal 10. FIG.

The acquisition unit 23 is a functional unit that acquires various data from the user terminal 10 . For example, when the user inputs voice at the user terminal 10, the voice data is acquired. Voice data is, for example, digital data related to the user's voice (voice) for 10 seconds.

The conversion unit 24 is a functional unit that converts the audio data acquired by the acquisition unit 23 into audio frequency data. Fourier transform is used for such conversion into audio frequency data, as described in Patent Document 1 (Japanese Patent No. 6029223), for example. In order to speed up the calculation on the computer, it is preferable to use the method of "Cooley-Tukey type FFT algorithm" in programming.

The classification unit 25 is a functional unit that distributes the audio frequency data converted by the conversion unit 24 in a distribution chart including a plurality of areas divided by scales and octaves. The distribution chart may display the distribution of the audio frequency data visually and in an easy-to-understand manner, and may be either tabular or graphical.

For the audio frequency data converted by the conversion unit 24, the determining unit 26 selects a scale that occupies the largest proportion in the octave (hereinafter referred to as the largest scale) and a scale that occupies the smallest proportion in the octave (hereinafter referred to as the smallest scale). It is the function part that decides. For example, as described in Patent Document 1, the magnitude (dB) of each scale in each frequency band per sampling time is measured, they are simply added in each frequency band, and within each octave, each scale The area ratio is defined as the total ratio of dB in the frequency band of , and the maximum scale and the minimum scale are determined based on this area ratio.

For example, within a certain octave, the sum of dB in each scale frequency band is "C: 1000 dB, C#: 3000 dB, D: 2000 dB, D#: 2500 dB, E: 1500 dB, F: 2500 dB, F#: 500 dB, G: 0 dB, G#: 100dB, A: 600dB, A#: 300dB, B: 1000Db. In this case, the proportion of the scale in the octave is "C: 6.6%, C#: 20.0%, D: 13.3%, D#: 16.7%, E: 10.0%, F: 16.7%, F#: 3.3%, G: 0.0 %, G#: 0.7%, A: 4.0%, A#: 2.0%, B: 6.7%". More accurate data can be obtained by calculating the total sum of each frequency band/sampling time.

The diagnosis unit 27 is a functional unit that diagnoses the user's voice based on the maximum scale and minimum scale determined by the determination unit 26 . During this diagnosis, the diagnosis master data 21C is referred to, and the diagnosis history and diagnosis results are saved as diagnosis history data 21A and diagnosis result data 21B.

The authentication unit 28 is a functional unit that authenticates users. The method of user authentication may be a method of registering users in advance, but here, a browser code that has the same value if the same terminal is used is used.

In addition, the voice diagnostic device 20 is assumed to have various functional units that a general computer has. For example, an input/output unit 29 for inputting/outputting various data may be provided.

[Data example]
FIG. 4A is an internal configuration diagram of diagnostic history data 21A stored in the storage unit 21. As shown in FIG. The diagnosis history data 21A is data indicating the history of voice diagnosis. Specifically, as shown in FIG. 4A, "diagnosis ID", "browser code", "diagnosis date and time", etc. are associated and stored. "Diagnosis ID" is a unique identification number for each diagnosis. "Browser code" is a code that has the same value if the same terminal is used, and is specifically a browser fingerprint (described later). "Date and time of diagnosis" is the date and time when the diagnosis was made.

FIG. 4B is an internal configuration diagram of the diagnosis result data 21B stored in the storage unit 21. As shown in FIG. The diagnosis result data 21B is data indicating the diagnosis result. For the diagnosis ID in the diagnosis history data 21A, the diagnosis result is stored for each scale divided by seconds. Specifically, as shown in FIG. 4B, "diagnosis ID", "second", "value of each scale", etc. are associated and stored. "Second" is the number of seconds when the sound was measured. For example, when the user terminal 10 measures the voice for 10 seconds, it becomes 1, 2, 3, . . . The "value of each scale" is the voice power in each scale, for example, the loudness (dB) of the voice.

FIG. 4C is an internal configuration diagram of the diagnostic master data 21C stored in the storage unit 21. As shown in FIG. The diagnostic master data 21C is a master for sorting messages to be returned according to diagnostic result values when returning diagnostic results to the user terminal 10 . Specifically, as shown in FIG. 4(C), "scale", "color", "meaning", "type", "feature", "task", etc. are associated and stored. For example, the voice color corresponding to scale C is red R. "Meaning" is the meaning of the scale (voice color). "Type" is the type of user's voice whose scale is the highest scale. The “feature” is information indicating features of the user whose scale is the maximum scale. "Task" is information indicating the task of the user whose scale is the smallest scale. The "type", "feature", and "task" are character information that can be visually recognized by the user, and the content is such that even a user with no knowledge of speech psychology can understand.

[Browser Fingerprint]
A short data string unique to a terminal, which is calculated to identify or identify a viewer of a website, is called a browser fingerprint. According to the browser fingerprint, it is possible to identify the terminal without using a function such as a cookie that permanently writes data to the terminal of the viewer. Algorithm for calculating the fingerprint is not particularly limited, but it is desirable to calculate based on a plurality of pieces of information that can be obtained from the browser of the user terminal 10 . In this embodiment, "Fingerprintjs2" is used to determine whether or not the current user is the same as the past user. According to such a browser fingerprint technology, it is possible not only to determine the frequency of use for each user, but also to determine whether or not it is the same person without specifying the individual.

[Distribution chart]
5A and 5B are distribution charts in the first embodiment, FIG. 5A showing a distribution chart in tabular form, and FIG. 5B showing a distribution chart in graphic form. The format is different, but the content is the same.

For example, in a tabular distribution chart, as shown in FIG. arranged vertically. M is magenta, V is violet, N is navy, B is blue, A is aqua, E is emerald green, and L is is lime green, Y is yellow, G is gold, O is orange, C is coral, and R is red. On the actual screen, each voice color is divided into 12 scales. The meaning of each scale (each voice color) is as shown in FIG.

In addition, in the distribution chart in tabular form, as shown in FIG. 5A, voice power (loudness) in each scale is distributed in the horizontal direction. Here, a case is illustrated in which the horizontal direction is divided into four regions. For example, the first area from the left is the octave of the frequency range of 64 to 123 Hz, the second area from the left is the octave of the frequency range of 128 to 246 Hz, the third area from the left is the octave of the frequency range of 4096 to 7900 Hz, The fourth region from the left is the octave of the frequency range from 8192-15800 Hz.

On the other hand, in the diagrammatic (circular) distribution chart, as shown in FIG. , R are arranged in the circumferential direction. Here, too, the case of dividing into four areas by four circles is illustrated. For example, the area on the first circumference from the inside is an octave of the frequency range from 64 to 123 Hz, the area on the second circumference from the inside is an octave from the frequency range from 128 to 246 Hz, and the area on the third circumference from the inside is an octave in the frequency range from 4096 to 7900 Hz, and the fourth area on the circumference from the inside is an octave in the frequency range from 8192 to 15800 Hz.

It is possible to appropriately select which distribution chart to use. For example, a tabular distribution chart is useful when diagnosing parts of the human body in association with the 12-tone scale, as indicated by dotted lines in FIG. 5(A). On the other hand, in the graphical distribution chart, as shown in FIG. 5B, the octave of the frequency range in the outer circle is conspicuous, so it can be displayed with emphasis.

[Screen transition example]
6 to 10 are screen transition diagrams of the user terminal 10 according to the first embodiment. When the browser of the user terminal 10 receives the web page data from the voice diagnosis device 20, it converts it into graphics and displays (renders) such a screen.

As shown in FIG. 6, the top screen D1 displays an overview of the voice diagnosis. When the "Diagnose Now" button B1 in FIG. 6 is pressed, the screen changes to the measurement screen D2 as shown in FIG. When the "measurement start" button B2 in FIG. 7 is pressed, the measurement time (for example, 10 seconds) is displayed with an indicator I as shown in FIG. "View" button B3 becomes a state in which it can be pressed. When the "view result" button B3 in FIG. 9 is pressed, a diagnosis result screen D3 is displayed as shown in FIG. When the "diagnose again" button B4 in FIG. 10 is pressed, the screen returns to the top screen D1.

[Diagnosis]
The diagnosis result screen D3 will be described in more detail below. As shown in FIG. 10, on the diagnosis result screen D3, your voice type 11, your voice has many colors 12, your voice has few colors 13, a tabular distribution chart 14A, and a diagrammatic distribution chart 14B. , your personality/characteristics 15 that can be understood from your voice, your tasks 16, and the like are displayed.

Your voice type 11 is information indicating the user's voice type. Specifically, it corresponds to the "type" associated with the maximum scale in the diagnostic master data 21C. The 'maximum scale' can be rephrased as 'the most common voice color in the user's voice', and the case of 'magenta' is exemplified here. Your voice type 11 is desirably relatively short character information that simply represents your voice type. For example, in the case of "magenta", it is displayed as "loved voice that will be forgiven even if you say something harsh".

Your voice color 12 is the user's voice color. Specifically, it corresponds to the top three voice colors when the total ratio of dB of the frequency band of each scale is arranged in descending order. Here, the most common voice color is "magenta", the second most common voice color is "violet", and the third most common voice color is "navy".

Low in your voice color 13 is the low voice color of the user's voice. Specifically, it corresponds to the lower three voice colors when the total ratio of dB in the frequency band of each scale is arranged in descending order. In this example, the least voice color is "red", the second least voice color is "aqua", and the third least voice color is "green".

Your personality/characteristics 15 that can be understood from the voice is information indicating the characteristics of the user. Specifically, it corresponds to the "feature" associated with the maximum scale in the diagnostic master data 21C. As described above, the 'maximum scale' can be rephrased as 'the most common voice color of the user's voice', and the case of 'magenta' is exemplified here. Your personality/characteristics 15 that can be understood from your voice is preferably relatively long character information. For example, in the case of "magenta", it is displayed as "From a broad perspective, you can understand people and make them understand you...".

Your task 16 is information indicating the user's task. Specifically, in the diagnostic master data 21C, it corresponds to the "problem" associated with the lowest scale. The 'lowest scale' can be translated as 'the least voice color in the user's voice', here exemplified by the case of 'red'. Your assignment 16 should also be relatively long textual information. For example, in the case of "red", a message such as "I tend to move too much and run out of energy..." is displayed.

In this way, on the diagnosis result screen D3, the diagnosis results become more detailed from top to bottom, and finally your problem 16 is displayed. As a result, even a user who does not have knowledge about the psychology of speech can self-diagnose speech by himself/herself to promote health.

[Example of operation]
FIG. 11 is a sequence diagram showing an operation example of the voice diagnosis system according to the first embodiment. The configuration of this system and its operation will be described below with reference to FIG.

First, when a URL (Uniform Resource Locator) is input at the user terminal 10 (step S1), the user terminal 10 issues a request to the voice diagnostic apparatus 20 . As a result, the top screen D1 is returned from the voice diagnosis device 20 to the user terminal 10, and the top screen D1 is displayed on the user terminal 10 (steps S2→S3).

Next, when the user presses the "diagnose now" button B1 on the top screen D1 displayed on the user terminal 10 (step S4), the user terminal 10 notifies the audio diagnosis device 20 of the browser code. As a result, the browser code is acquired in the voice diagnosis apparatus 20, and the browser code is used to determine whether the person is the same person, and limit the number of executions per predetermined period (for example, one day) (step S5→S6). →S7).

Specifically, by referring to the diagnosis history data 21A stored in the storage unit 21, it is determined whether or not the current user is the same person as the past user. (e.g., 8 times/day). As a result, when the number limit is not exceeded, it is determined that the authentication has succeeded, the measurement screen D2 is returned from the voice diagnosis device 20 to the user terminal 10, and the measurement screen D2 is displayed on the user terminal 10 (step S7). →S8→S9). On the other hand, if the number of times limit is exceeded, it is determined that the authentication has failed, and the voice diagnosis device 20 notifies the user terminal 10 to that effect (steps S7→S2→S3).

Next, when the "measurement start" button B2 is pressed on the measurement screen D2 displayed on the user terminal 10 (step S10), a mode for measuring (recording) voice for 10 seconds, for example, is entered. As a result, when a voice is input from the headset H of the user terminal 10, the analog voice signal is converted into a digital voice signal by a technique such as PCM (Pulse Code Modulation) and stored as voice data (step S11). ). During that time, the elapsed time is displayed on the indicator I in the measurement screen D2.

Next, when the "view results" button B3 is pressed on the measurement screen D2 displayed on the user terminal 10 (step S12), the user terminal 10 notifies the audio diagnosis device 20 of audio data. As a result, voice data is acquired in the voice diagnostic apparatus 20, the acquired voice data is converted into voice frequency data by Fourier transform, and the transformed voice frequency data is distributed in the distribution chart (steps S13→S14→S15). ). Further, the maximum scale and the minimum scale are determined for the converted voice frequency data, and the sound is diagnosed based on the determined maximum scale and minimum scale (step S16→S17).

The diagnosis master data 21C is referred to during this diagnosis. For example, if the largest scale is scale B, information associated with scale B (eg, magenta, compassionate power, etc.) is extracted. If the lowest scale is scale C, information associated with scale C (for example, red, energy, etc.) is extracted. When the diagnosis history data 21A and the diagnosis result data 21B are saved (step S18), the diagnosis result screen D3 is provided from the voice diagnosis device 20 to the user terminal 10, and the diagnosis result screen D3 is displayed on the user terminal 10 (step S18). S19→S20).

The user can redo the diagnosis, such as when voice recording fails. That is, when the "diagnose again" button B4 is pressed on the diagnosis result screen D3 displayed on the user terminal 10 (step S21), the screen returns to the top screen D1 (step S2→S3). Diagnosis is repeated (steps S4→S5→...), and if the number of times of execution per day is exceeded, a message to that effect is displayed (steps S7→S2→S3).

As described above, according to the first embodiment, the diagnostic result is displayed in a form that is easy for the user to understand, simply by following the explanation on the screen displayed on the user terminal 10 and speaking out loud. That is, even if the user does not have knowledge of speech psychology, the user can easily understand the diagnosis result by reading the diagnosis result screen D3 from top to bottom. In particular, since your personality/characteristics 15 that can be understood from your voice and your task 16 are relatively long sentences, you can make a self-diagnosis as if you were receiving advice from a speech psychologist.

In the above description, both the maximum scale and the minimum scale are determined, and both your personality/characteristics 15 that can be understood from your voice and your task 16 are provided to the user terminal 10 as an example. , the invention is not limited thereto. That is, even if only one of the maximum scale and the minimum scale is determined, and only one of your personality/characteristics 15 that can be understood from your voice and your task 16 is provided to the user terminal 10 good.

Also, although not mentioned in the above description, the current diagnosis result and the past diagnosis result may be compared to diagnose the degree of improvement of the user's problem. For example, when the "Diagnose again" button B4 is pressed and a diagnosis is made in the same procedure, the degree of improvement in the minimum scale magnitude (dB) compared to the previous time is quantified and displayed on the diagnosis result screen D3. may be displayed in As a result, it is possible to present the degree of improvement of the issue in an easy-to-understand form for the user, so that the user can easily make continuous efforts to improve the issue.

<<Second embodiment>>
A second embodiment will be described below. In addition, in the following description, the parts different from the first embodiment will be mainly described, and the detailed description of the same parts will be omitted.

As already explained, voice diagnosis is a method for promoting health by knowing one's own voice and adjusting one's voice. Conventionally, even if a voice diagnosis results in a prescription for improving oneself, there are cases in which it is not clear whether the voice has actually improved. For example, even if you know a prescription that suits you, such as getting in touch with nature, taking a walk, or listening to music, putting it into practice is a high hurdle and difficult to continue. Therefore, in the second embodiment, the following method is adopted in order to make it easier for the user to promote his or her own health.

[Full Sound Voice]
The inventor has so far diagnosed the voices of about 20,000 people and provided counseling. We visualized the person's thinking pattern with voice conversion software and identified the relationship between the musical scale and the area of the mind. In addition, by deciphering up to 8 octaves, including the inaudible range, it was found that it is important to have a voice that resonates with a good balance of all overtones in the entire sound range (hereinafter referred to as a full sound voice). I found that opening the soft palate was the key to becoming a . The soft palate is the soft part behind the hard palate in the oral cavity. In order to open the soft palate, it is effective to work to change the perception of weakness by producing the sound of the voice color of the weak field (task) or the voice color of its complementary color.

[Voice diagnosis device]
FIG. 12 is a functional block diagram of the speech diagnosis device 20 according to the second embodiment. This voice diagnosis device 20 includes a zone determination section 30 as shown in FIG. The storage unit 21 also stores zone data 21E, training sound data (hereinafter referred to as training sound) 21F, and the like.

The zone determination unit 30 determines a vocalization training zone for improving the degree of opening of the soft palate based on the minimum scale determined by the determination unit 26 . A training sound 21F corresponding to each utterance training zone (for each scale) is stored in the storage unit 21 . The training sound 21F is, for example, digital data relating to harmonic overtone sounds. Thereby, the providing unit 22 provides the user with the training sound 21F corresponding to the vocalization training zone determined by the zone determining unit 30, and causes the user to vocalize in accordance with the training sound 21F. Provide users with data-driven information that indicates the effectiveness of their training.

[Data example]
FIG. 13 is a conceptual diagram of zone data 21E in the second embodiment. The zone data 21E is data that defines vocalization training zones for improving the degree of opening of the soft palate, and associates scales with vocalization training zones using a circular ring diagram.

For example, as shown in FIG. 13, the scale Do corresponds to the magenta and red zones, the scale Re corresponds to the red and orange zones, the scale Mi corresponds to the orange and yellow zones, and the scale F. corresponds to the yellow and green zones, the scale So corresponds to the green and blue zones, the scale La corresponds to the blue and magenta zones, and the scale C corresponds to the magenta zone.

[Distribution chart]
FIG. 14 is a schematic diagram showing a distribution chart in the second embodiment. Here, the 12-tone scale is divided into "do-re#", "mi-so", and "so#-si", and the sound range is divided into "low range", "middle range" and "high range". In the drawing, the frequencies (Hz) drawn corresponding to the "low range", "middle range", and "high range" are examples, and can be set to any octave range. By displaying such a distribution chart on the user terminal 10, the part where the sound of the voice is weak (which sound range is hard to come out) is confirmed, and the sound of the voice is improved while vocalizing along with the training sound 21F.

[Screen transition example]
FIG. 15 shows a training screen D4 displayed on the user terminal 10 according to the second embodiment, where FIG. 15(A) shows before training and FIG. 15(B) shows after training. Although only the distribution chart portion is shown in this figure, various buttons may be provided in addition to character information indicating training methods and training effects.

For example, before training, as shown in FIG. 15(A), only about 30% of the vocal range is voiced. When the range distribution is biased like this, there is a zone (for example, magenta zone) in which the vocalization is not sufficiently performed, and the soft palate is not very open. Therefore, in such a case, the user is made to listen to the training sound 21F corresponding to the magenta zone and vocalize along with the training sound 21F.

As a result, after the training, as shown in FIG. 15(B), the vocal range is about 100%. The degree of opening of the soft palate corresponds to the width of the corrugated area. It can be said that the soft palate is more open when the vocal waveforms of 12 colors are evenly distributed. In the present invention, such voice is called "full sound voice". By training your vocalizations to produce a full sound voice, you can shape your voice and promote health.

[Example of operation]
FIG. 16 is a sequence diagram showing an operation example of the voice diagnosis system according to the second embodiment. Although illustration is omitted here, it is assumed that a "train" button is provided at the end of the diagnosis result screen D3. Of course, it is sufficient if the operation for training is possible, and is not limited to such button operation.

First, when the "Train" button is pressed on the diagnostic result screen D3 displayed on the user terminal 10 (steps S31→S32), the user terminal 10 notifies the audio diagnostic device 20 of the fact, and the audio diagnostic device 20 A vocal training zone is determined at . For example, if the lowest scale is scale B (the sound of B), magenta is the weak voice color. Therefore, in such a case, the magenta zone is determined as the vocalization training zone. Alternatively, the yellow zone, which has a complementary color relationship with the magenta zone, may be determined as the vocalization training zone. A complementary color is a color on the opposite side of the color correlation, and can be determined using the ring diagram of FIG.

When the vocalization training zone is determined in this way, the training sound 21F corresponding to the vocalization training zone is specified, and the training screen D4 is provided from the speech diagnosis device 20 to the user terminal 10 (steps S33→S34→S35). . Although the content of the training screen D4 is not particularly limited, for example, a distribution chart as shown in FIG. 15 is provided. In addition, it is assumed that a message such as "Keep vocalizing along with the training sound" and a "Start training" button are provided.

Next, when the "training start" button is pressed on the training screen D4 displayed on the user terminal 10 (steps S36→S37), the training sound 21F is output from the headset H for 3 minutes, for example (step S38). When the user speaks along with the training sound 21F, the voice is recorded, and the voice data is notified from the user terminal 10 to the voice diagnostic device 20 in real time (step S38). As a result, after the voice diagnosis device 20 performs the same processing as steps S13 to S18 in FIG. 11, the voice diagnosis device 20 provides the user terminal 10 with training results such as a distribution chart (steps S39→S40). .

As a result, the training result is reflected in real time on the training screen D4 displayed on the user terminal 10 (step S41). That is, when the user speaks along with the training sound 21F output from the headset H, the resonance of the voice is reflected in the distribution chart in real time. Such processing is repeated (steps S42→S38→...), and after 3 minutes have passed, the training ends.

As described above, according to the second embodiment, after diagnosing the voice as in the first embodiment, the user himself/herself can work to improve the problem. In other words, the user can train his or her voice while looking at the waveform of the voice, and can confirm the effect of the training on the screen, just by speaking along with the training sound 21F. It is possible.

Currently, voice training is generally performed in an analog way under the guidance of a voice trainer, making it difficult to measure the effects. It can be said that a system like the present invention, in which a person can train by himself/herself using digital technology and can visually recognize the effect of the training in a chart, has a very high practical value.

In the above description, the same voice diagnosis as in the first embodiment (steps S13 to S18 in FIG. 11) is performed (step S39), but the present invention is not limited to this. That is, in the second embodiment, since the main aim is to address the problem, it is sufficient to determine at least the minimum scale, and it is not essential to determine the maximum scale. However, once the maximum scale is determined, you can understand your own personality and characteristics, so you can deepen your self-awareness and make it easier to work on improving your problems. Therefore, in the second embodiment as well, it is recommended to perform the same voice diagnosis as in the first embodiment (steps S13 to S18 in FIG. 11) from the viewpoint of enhancing the effect of training.

《Hardware configuration example》
FIG. 17 is a hardware configuration diagram of the audio diagnostic device 20 according to the embodiment of the present invention. Here, the voice diagnosis device 20 is illustrated, but the user terminal 10 is basically the same.

As already explained, the voice diagnosis device 20 is a computer. For example, as shown in FIG. 17, a processor 20A, a memory 20B, a storage section 20C, an IF (Interface) section 20D, an input/output section 20E, and a reading section 20F are communicably connected to each other via a bus 20I. The processor 20A is an arithmetic processing unit that performs various controls and operations. The memory 20B is a volatile memory that stores various data and programs. The storage unit 20C is a nonvolatile memory that stores various data and programs. The IF unit 20D is a functional unit that controls communication with other terminals. The input/output unit 20E is an input device for inputting data and an output device for outputting data. The reading unit 20F is a device that reads data and programs recorded on a computer-readable recording medium. Although not shown here, it goes without saying that a headset H may be connected to the speech diagnosis device 20 .

<<Characteristic configuration of the present invention and its effect>>
As described above, the speech diagnosis apparatus 20 according to the embodiment of the present invention is an apparatus for diagnosing speech, and includes the storage unit 21 that stores feature information and/or task information associated with each scale, and input by the user. a conversion unit 24 for converting the converted audio data into audio frequency data; a determination unit 26 for determining the scale having the maximum and/or minimum ratio in the octave for the audio frequency data converted by the conversion unit 24; A diagnosis unit 27 that diagnoses the user's voice based on the maximum scale and / or the minimum scale determined by the determination unit 26, and provides a screen explaining the voice diagnosis to the user to prompt the start of voice input, and a provision unit 22 for providing a user with the diagnosis result diagnosed by the diagnosis unit 27 . When the maximum scale is determined by the determination unit 26, the diagnosis unit 27 uses the characteristic information stored in the storage unit 21 in association with the maximum scale as information indicating the characteristics of the user. Further, when the minimum scale is determined by the determination unit 26, the task information stored in the storage unit 21 in association with the minimum scale is used as information indicating the user's task. As a result, the diagnostic result is displayed in a form that is easy for the user to understand, simply by following the explanation on the screen displayed on the user terminal 10 and speaking out. Therefore, even a user who does not have knowledge about speech psychology can self-diagnose speech by himself/herself to promote health.

Furthermore, after providing a screen explaining the voice diagnosis to the user (step S2), before the user instructs to start inputting voice (step S10), the user is authenticated by the browser fingerprint without identifying the individual. You may provide the authentication part 28 which carries out (step S5 ->S6 ->S7). As a result, the user can be appropriately authenticated at the required timing without prior user registration. Therefore, it is possible to group diagnosis results for each individual and accumulate diagnosis results for each person while ensuring anonymity. With such a browser fingerprint, it will be possible in the future to have extensibility such as a diagnostic method using AI. Since voice diagnosis is a diagnosis related to an individual's psychological state, health condition, or thought pattern, a mechanism for authenticating the user while ensuring anonymity is very important.

Further, a zone determination unit 30 may be provided for determining a vocal training zone for improving the opening of the soft palate based on the minimum scale determined by the determination unit 26 . In this case, the storage unit 21 stores the corresponding training sound 21F for each vocalization training zone. Further, the providing unit 22 provides the user with the training sound 21F corresponding to the vocalization training zone determined by the zone determination unit 30, and causes the user to vocalize in accordance with the training sound 21F. provide the user with information (e.g., a distribution chart) that indicates the effectiveness of the training based on As a result, the degree of opening of the soft palate is improved and the voice changes just by vocalizing along with the training sound 21F, so that the user can promote his or her own health.

The present invention can be realized as a speech diagnosis method in which each characteristic function unit provided in the speech diagnosis apparatus 20 is used as each step, or as a speech diagnosis program for causing a computer to execute each step. You can also Of course, such a voice diagnosis program can be installed in a computer via a computer-readable recording medium or via a network such as the Internet network N.

Further, although the cloud-type voice diagnosis system is exemplified in the first and second embodiments, the present invention is not limited to this. For example, if each functional unit included in the voice diagnostic device 20 is installed in the user terminal 10, the same voice diagnosis can be performed in a stand-alone type. Regardless of cloud type or stand-alone type, as long as similar functions are realized, they belong to the technical scope of the present invention.

Also, the voice diagnosis device 20 in the second embodiment can be rephrased as a speech health promotion device. That is, this vocalization health method promotion device is a device for promoting health by training vocalization, and stores a training sound 21F for each vocalization training zone (every scale) for improving the degree of opening of the soft palate. a conversion unit 24 for converting voice data input by a user into voice frequency data; and a determination unit 26 for determining the scale having the smallest ratio in the octave for the voice frequency data converted by the conversion unit 24. , while providing the user with a zone determination unit 30 that determines the vocal training zone based on the minimum scale determined by the determination unit 26, and a training sound 21F corresponding to the vocal training zone determined by the zone determination unit 30, A providing unit 22 for causing the user to vocalize along with the training sound 21F and providing the user with information (for example, a distribution chart) indicating the effect of the training based on the audio data of the voice. As a result, the degree of opening of the soft palate is improved and the voice changes just by vocalizing along with the training sound 21F, so that the user can promote his or her own health.

[Other embodiments]
While embodiments of the present invention have been described above, the discussion and drawings forming part of this disclosure are to be considered illustrative and should not be construed as limiting. Various alternative embodiments, implementations and operational techniques will become apparent to those skilled in the art from this disclosure.

10 user terminal 14A distribution chart in tabular form 14B distribution chart in graphic form 20 voice diagnosis device 21 storage unit 21A diagnosis history data 21B diagnosis result data 21C diagnosis master data 21D HTML data 21E zone data 21F training sound 22 provision unit 23 acquisition unit 24 Conversion unit 25 Classification unit 26 Determination unit 27 Diagnosis unit 28 Authentication unit 29 Input/output unit 30 Zone determination unit

Claims (3)

A vocalization health promotion device for promoting health by training vocalization,
a storage unit for storing, for each vocalization training zone for improving the degree of opening of the soft palate, a training sound that is sound data of a scale corresponding to the vocalization training zone;
a conversion unit for converting voice data input by a user into voice frequency data;
With respect to the voice frequency data converted by the conversion unit, the ratio of the sum of voice power in the frequency band corresponding to the scale to the total sum of voice power in the frequency band corresponding to the octave to which the scale belongs is minimized. A decision unit that decides the scale of
a zone determination unit that determines the vocalization training zone based on the minimum scale determined by the determination unit;
While providing the user with a training sound corresponding to the vocalization training zone determined by the zone determining unit, the user is made to speak in accordance with the training sound, and the effect of the training is evaluated based on the audio data of the voice. a providing unit for providing the user with information indicating
The zone determination unit associates the scale with the voice color using a circular ring diagram, and selects a voice color corresponding to the minimum scale, or a voice color complementary to the voice color corresponding to the minimum scale. and determining the vocal training zone based on the vocal health method promotion device. A vocal health promotion method for promoting health by training vocalization,
the computer
a storage step of storing, for each vocalization training zone for improving the degree of opening of the soft palate, a training tone that is tone data of a scale corresponding to the vocalization training zone;
a converting step of converting voice data entered by a user into voice frequency data;
With respect to the voice frequency data converted in the converting step, the ratio of the sum of voice power in the frequency band corresponding to the scale to the total sum of voice power in the frequency band corresponding to the octave to which the scale belongs is minimized. a determining step of determining the scale to be
a zone determination step of determining the vocalization training zone based on the minimum scale determined in the determining step;
While providing the user with a training sound corresponding to the vocalization training zone determined in the zone determination step, the user is made to speak in accordance with the training sound, and the effect of the training is evaluated based on the audio data of the voice. and performing a providing step of providing the user with information indicative of
In the zone determination step, the scale and the voice color are associated using a circular ring diagram, and the voice color corresponding to the minimum scale or the voice color complementary to the voice color corresponding to the minimum scale is selected. and determining the vocal training zone based on the vocal health promotion method. A vocal health promotion program for promoting health by training vocalization,
to the computer,
a storage step of storing, for each vocalization training zone for improving the degree of opening of the soft palate, a training tone that is tone data of a scale corresponding to the vocalization training zone;
a converting step of converting voice data entered by a user into voice frequency data;
With respect to the voice frequency data converted in the converting step, the ratio of the sum of voice power in the frequency band corresponding to the scale to the total sum of voice power in the frequency band corresponding to the octave to which the scale belongs is minimized. a determining step of determining the scale to be
a zone determination step of determining the vocalization training zone based on the minimum scale determined in the determining step;
While providing the user with a training sound corresponding to the vocalization training zone determined in the zone determination step, the user is made to speak in accordance with the training sound, and the effect of the training is evaluated based on the audio data of the voice. and performing a providing step of providing the user with the information indicated;
In the zone determination step, the scale and the voice color are associated using a circular ring diagram, and the voice color corresponding to the minimum scale or the voice color complementary to the voice color corresponding to the minimum scale is selected. and determining the vocal training zone based on the vocal health method promotion program.

Images