A learning monitoring method and device applied to a smart earphone and an electronic device

Abstract

The application provides a learning monitoring method and device applied to an intelligent earphone and an electronic device, and relates to the technical field of data processing. In the method, first voice input information is obtained, the first voice input information being voice input information of a user in a first time period; the number of times of remembering a first vocabulary is obtained according to the first voice input information; second voice input information is obtained, the second voice input information being voice input information of the user in a second time period, the second time period being later than the first time period; the number of times of remembering a second vocabulary is obtained according to the second voice input information, the first vocabulary and the second vocabulary being the same vocabulary; a change trend of the number of times of remembering is determined according to the number of times of remembering the first vocabulary and the number of times of remembering the second vocabulary; and if the change trend of the number of times of remembering is an upward trend, the user is prompted to have a decreased memory efficiency according to a preset mode. By implementing the technical solution provided by the application, the user can accurately know that the memory efficiency is decreased.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a learning monitoring method, device, and electronic device for use in smart headphones. Background Technology

[0002] With the development of artificial intelligence and natural language processing technologies, speech recognition technology is becoming increasingly mature. Users can interact with devices through voice input, and the devices can recognize the user's voice commands and execute corresponding operations.

[0003] In actual memorization, users often use smart headphones as voice input devices to aid in memorizing words. However, there are certain differences in how well users memorize vocabulary. Users may find it easier to remember certain words at certain times, while at other times they may find it less easy; fundamentally, this is because their memory efficiency decreases. However, because users are fully immersed in the process of memorizing, they are unable to accurately perceive this decrease in memory efficiency.

[0004] Therefore, there is an urgent need for a learning monitoring method, device, and electronic device for use in smart headphones. Summary of the Invention

[0005] This application provides a learning monitoring method, device, and electronic device for smart headphones, which helps users accurately identify their declining memory efficiency.

[0006] A first aspect of this application provides a learning monitoring method for smart headphones, the method comprising: acquiring first voice input information, wherein the first voice input information is voice input information of a user in a first time period; obtaining the number of times a first word is memorized based on the first voice input information; acquiring second voice input information, wherein the second voice input information is voice input information of a user in a second time period, the second time period being later than the first time period; obtaining the number of times a second word is memorized based on the second voice input information, wherein the first word and the second word are the same word; determining a trend of change in the number of memorizations based on the number of memorizations of the first word and the number of memorizations of the second word; and if the trend of change in the number of memorizations is determined to be an upward trend, then prompting the user that the memorization efficiency has decreased according to a preset method.

[0007] By employing the above technical solution, the number of times the first word was memorized is obtained by acquiring the first voice input information in the first time period, and the number of times the second word was memorized is obtained by acquiring the second voice input information in the second time period. Next, by analyzing the number of times the first and second words were memorized, the trend of the user's memorization frequency can be determined. When the trend of the number of memorization frequencies is upward, a preset notification will alert the user that their memorization efficiency has decreased. Therefore, by monitoring the user's vocabulary memorization process in real time, the goal of analyzing the user's memorization efficiency based on the trend of the number of memorization frequencies can be achieved, allowing the user to accurately identify a decline in their memorization efficiency.

[0008] Optionally, obtaining the first voice input information specifically includes: receiving audio data input by a user, wherein the audio data is audio data of the user reciting vocabulary; preprocessing the audio data to obtain the first voice input information, wherein the preprocessing includes data cleaning, data denoising, and data normalization.

[0009] By adopting the above technical solution, data cleaning, denoising, and normalization preprocessing can be performed on the received and processed audio data, effectively improving the quality of the audio data and reducing noise and interference. Preprocessing can eliminate potential interference factors such as background noise and unclear pronunciation, thereby making the subsequent calculation of memorization counts more accurate and reliable. Through audio data cleaning and denoising, the quality of the user's input audio is improved, resulting in a better user experience when memorizing vocabulary. Normalization processing can transform the user's speech data from different time periods into a uniform scale, which is beneficial for more accurate analysis and comparison.

[0010] Optionally, obtaining the number of times the first word is memorized based on the first voice input information specifically includes: performing voice recognition on the first voice input information to obtain corresponding text information, the text information including multiple words, the first word being any one of the multiple words; performing word frequency statistics on the text information to obtain the number of times the first word appears; and determining the number of times the first word is memorized based on the number of times the first word appears.

[0011] By employing the aforementioned technical solution, speech data is converted into text information using speech recognition technology, reducing data inaccuracies caused by speech transcription errors and improving the accuracy of memorization counts. Through word frequency statistics and determination of memorization counts, the frequency of word occurrences and memorization counts can be automatically calculated, significantly improving processing efficiency. This method not only determines the memorization count of individual words but also comprehensively analyzes the occurrence and distribution of all words in the text information, facilitating more comprehensive memory and learning analysis. By performing word frequency statistics and determining the memorization counts of words in the text information, users can better understand which words they frequently use and learn, thereby guiding them to conduct more effective vocabulary learning and memorization.

[0012] Optionally, determining the trend of memory count changes based on the memory counts of the first word and the second word specifically includes: inputting the memory counts of the first word and the second word, as well as the first time period and the second time period, into the ARIMA model to obtain a memory curve, wherein the ARIMA model pre-stores the historical memory counts of multiple words; and analyzing the memory curve to determine the trend of memory count changes.

[0013] By employing the aforementioned technical solutions and utilizing the ARIMA model, future trends in memory recall can be predicted based on historical recall frequency. This prediction helps users understand the changing trends in their memory efficiency during the learning process, allowing for timely adjustments to their learning strategies. The ARIMA model can be used for long-term monitoring of a user's memory efficiency, continuously tracking their learning progress and trends, providing guidance for long-term learning. Analysis of memory curves provides a more intuitive understanding of changes in a user's memory efficiency, helping them better understand their memory characteristics and learning process. This method can analyze and predict not only individual words but also compare and analyze multiple words simultaneously, helping users better understand the learning difficulty and efficiency of different words.

[0014] Optionally, the preset method is to play a prompt message to the user, the prompt message including the memory duration, the prompt message being used to remind the user to take a break.

[0015] By employing the above technical solution and displaying prompts to users, timely feedback on their memory efficiency can be provided, allowing users to understand their learning status and thus better adjust their learning strategies. The prompts include the duration of memory preparation, reminding users to schedule breaks appropriately during study to avoid over-fatigue. This rest reminder is beneficial for protecting users' physical health and improving learning efficiency. Timely feedback and rest reminders help users better adjust their learning strategies and improve learning efficiency. These prompts can also serve as a motivational tool, encouraging users to maintain enthusiasm and motivation during the learning process. Displaying prompts enhances the user experience, making users feel more comfortable and comfortable.

[0016] Optionally, in response to a user's word playback request; the word audio information is played according to the word playback request; the user's voice input information is obtained, the voice input information including the first voice input information and the second voice input information; the similarity between the word audio information and the voice input information is calculated; if it is confirmed that the similarity is less than a preset similarity threshold, voice correction information is played to correct the user's voice input information.

[0017] By employing the above technical solution, and responding to the user's vocabulary playback request by playing the vocabulary audio information, users can more easily acquire the pronunciation and correct spelling of words. By calculating the similarity between the vocabulary audio information and the voice input information, and determining whether to play voice correction information based on the similarity, users can better master the pronunciation and spelling of words. This allows for adaptive adjustments based on the user's actual learning progress, making learning more personalized. Playing voice correction information can promptly correct users' pronunciation and spelling errors, thereby improving learning efficiency and avoiding subsequent learning problems caused by incorrect pronunciation and spelling. By comparing the similarity between the vocabulary audio information and the user's voice input information, users can be guided to learn more intelligently, enhancing the learning experience and learning outcomes.

[0018] Optionally, if it is determined that the trend of the number of times the memory is changing is downward, then incentive information is played to the user, which is used to prompt the user to maintain the current memory efficiency.

[0019] By employing the above technical solution, if the trend of memory recall frequency is determined to be downward, indicating high user memory efficiency, then incentive messages are displayed to the user, prompting them to maintain their current memory efficiency. This incentive measure can enhance user confidence and motivation, encouraging them to continue their efforts. Timely feedback and incentives can increase user stickiness to the device, improving user learning enthusiasm and participation. Continuous incentives and feedback can help users develop good learning and memory habits, which has a positive impact on their long-term learning and development. Users can adjust their learning strategies based on the incentive messages to better cope with changes in memory efficiency and improve learning outcomes.

[0020] A second aspect of this application provides a learning monitoring device for smart headphones. The learning monitoring device includes an acquisition module and a processing module. The acquisition module is used to acquire first voice input information, which is voice input information from a user during a first time period. The processing module is used to obtain the number of times a first word is memorized based on the first voice input information. The acquisition module is also used to acquire second voice input information, which is voice input information from a user during a second time period, which is later than the first time period. The processing module is also used to obtain the number of times a second word is memorized based on the second voice input information, where the first word and the second word are the same word. The processing module is also used to determine a trend in the number of times the number of times the first word and the second word are memorized. Furthermore, if the trend in the number of times ...

[0021] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described above.

[0022] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described above.

[0023] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By acquiring the first voice input information in the first time period, the number of times the first word was memorized can be easily obtained. Similarly, by acquiring the second voice input information in the second time period, the number of times the second word was memorized can be easily obtained. Next, by analyzing the number of times the first and second words were memorized, the trend of the user's memorization frequency can be determined. When the trend of the number of memorization frequencies is upward, a preset notification will alert the user that their memorization efficiency has decreased. Therefore, by monitoring the user's vocabulary memorization process in real time, the goal of analyzing the user's memorization efficiency based on the trend of the number of memorization frequencies can be achieved, allowing the user to accurately identify a decline in their memorization efficiency.

[0025] 2. By using the ARIMA model, future memory frequency trends can be predicted based on historical memory recall patterns. This prediction helps users understand the changing trends in their memory efficiency during the learning process, allowing for timely adjustments to learning strategies. The ARIMA model can be used for long-term monitoring of a user's memory efficiency, continuously tracking their learning progress and trends, providing guidance for long-term learning. Analysis of memory curves provides a more intuitive understanding of changes in a user's memory efficiency, helping them better understand their memory characteristics and learning process. This method can analyze and predict not only individual words but also compare and analyze multiple words simultaneously, helping users better understand the learning difficulty and efficiency of different words.

[0026] 3. By calculating the similarity between vocabulary speech information and voice input information, and determining whether to play speech correction information based on the similarity, the system helps users better master the pronunciation and spelling of vocabulary. This allows for adaptive adjustments based on the user's actual learning progress, making learning more personalized. Playing speech correction information can promptly correct users' pronunciation and spelling errors, thereby improving learning efficiency and preventing subsequent learning problems caused by incorrect pronunciation and spelling. By comparing the similarity between vocabulary speech information and the user's voice input information, the system can more intelligently guide users' learning, enhancing the learning experience and learning outcomes. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a learning monitoring method for smart headphones, provided as an embodiment of this application.

[0028] Figure 2 This is a schematic diagram of a learning monitoring device for smart headphones, provided as an embodiment of this application.

[0029] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0030] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 31. Processor; 32. Communication bus; 33. User interface; 34. Network interface; 35. Memory. Detailed Implementation

[0031] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0032] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0033] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0034] With the continuous development of technology, artificial intelligence and natural language processing technologies have made significant progress. The maturity of these technologies has led to the increasingly widespread application of voice recognition technology in daily life and work. Today, users can interact with various devices through voice input, and these devices can recognize the user's voice commands and execute corresponding operations. For example, devices such as smart speakers, smart TVs, and smart headphones all have voice recognition capabilities, providing users with a more convenient user experience.

[0035] In the process of memorizing words, users typically employ various tools and methods to aid their learning. Among these, smart headphones, as a relatively new voice input device, have gradually gained widespread acceptance and are used for word memorization. However, users have found that the effectiveness of vocabulary memorization varies from person to person. Some users may be able to easily memorize a large number of words at certain times, while finding it difficult at other times. This difference is often due to a decrease in the user's memory efficiency.

[0036] However, when memorizing vocabulary, users are often so engrossed in the process that they may not accurately perceive a decline in their memorization efficiency. If a user struggles for an extended period without achieving good results, it can lead to frustration and a loss of confidence in vocabulary memorization. Therefore, a technological solution is needed that can monitor user memorization efficiency in real time and provide corresponding prompts, helping users to promptly recognize any decline in efficiency and take appropriate measures to improve it.

[0037] To address the aforementioned technical problems, this application provides a learning monitoring method for smart headphones, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a learning and monitoring method for smart headphones, provided as an embodiment of this application. The learning and monitoring method, applied to smart headphones, includes steps S110 to S160, as follows:

[0038] S110. Obtain first voice input information, which is the user's voice input information in the first time period.

[0039] Specifically, when a user wears smart headphones to learn and memorize vocabulary, the headphones will play vocabulary words according to the user's needs to facilitate memorization. Memorization methods include, but are not limited to, repeating and listening aloud, and the vocabulary includes, but is not limited to, Chinese words and English words. For example, if a user is a university student preparing for the CET-4 (College English Test Band 4), they can wear smart headphones to repeat CET-4 vocabulary words. In this case, the voice input information can be the CET-4 vocabulary words that the user repeats, such as "abandon."

[0040] In one possible implementation, obtaining the first voice input information specifically includes: receiving audio data input by a user, wherein the audio data is audio data of the user reciting vocabulary; and preprocessing the audio data to obtain the first voice input information, wherein the preprocessing includes data cleaning, data denoising, and data normalization.

[0041] Specifically, the smart headphones receive audio data input by the user, meaning the user records their voice using the smart headphones. This audio data is the user's audio data while memorizing vocabulary, allowing the smart headphones to capture the audio data of this process. Preprocessing is a step in data processing that involves transforming the raw audio data into a form that can be further analyzed. Data cleaning includes removing silences, shortening excessively long pauses, and removing noise such as coughs and laughter to ensure the data contains only valid speech information. Noise denoising further removes or reduces background noise in the audio, such as wind noise, traffic noise, or other environmental noise. This step is crucial for extracting clear speech input information. Data normalization is to transform the audio data into a uniform range or standard, which helps improve the accuracy of subsequent analysis. For example, this may include volume adjustment, frequency adjustment, etc.

[0042] For example, suppose a user is using smart headphones to memorize English words. The smart headphones will record your voice and then, through preprocessing steps, transform the audio data into a form that can be analyzed. In this process, data cleaning may remove irrelevant sounds, such as the user's cough, laughter, or background music; data denoising may eliminate environmental noise, such as wind or traffic noise; and data normalization may adjust the user's volume and tone of voice to more accurately identify and classify the speech input.

[0043] S120. Based on the first voice input information, obtain the number of times the first word has been memorized.

[0044] Specifically, after the smart earphones receive the first voice input information, they will determine the number of times the first word is memorized based on the first voice input information. The first voice input information may contain the voice data of at least one word, and the first word can be any one of the at least one words. The number of times the first word is memorized is determined by the number of times it appears in the voice data of the at least one word.

[0045] In one possible implementation, the number of times the first word is memorized is obtained based on the first voice input information, specifically including: performing voice recognition on the first voice input information to obtain corresponding text information, the text information including multiple words, the first word being any one of the multiple words; performing word frequency statistics on the text information to obtain the number of times the first word appears; and determining the number of times the first word is memorized based on the number of times the first word appears.

[0046] Specifically, the above process describes how a smart headset determines the number of times a first word is memorized based on the first voice input information. First, the smart headset performs speech recognition on the first voice input information to obtain the corresponding text information. Next, the smart headset performs word frequency statistics on the text information to obtain the number of times the first word appears. Finally, the smart headset determines the number of times the first word is memorized based on the number of times it appears. Speech recognition is a technology that converts sound into text. In this process, the speech recognition engine converts the received audio data into corresponding text information. This text information can be words, phrases, short sentences, or sentences, depending on the user's memory application scenario. After converting audio data into text information, speech recognition technology obtains text information that includes multiple words. These multiple words are all the words that appear in the voice input. For example, if a word appears more frequently, it can be inferred that the number of times that word is memorized is also higher.

[0047] S130. Obtain second voice input information, which is the user's voice input information in a second time period, which is later than the first time period.

[0048] Specifically, after acquiring the first voice input information, the smart earphones will acquire a second voice input information, which is acquired after a preset time period. The preset time period can be the time interval between two consecutive uses of the smart earphones to memorize vocabulary on a given day. The first time period corresponds to the time period for acquiring the first voice input information, and the second time period corresponds to the time period for acquiring the second voice input information. The smart earphones will also perform preprocessing on the second voice input information, similar to the preprocessing process described above, which will not be repeated here.

[0049] S140. Based on the second voice input information, obtain the number of times the second word is memorized. The first word and the second word are the same word.

[0050] Specifically, the smart earphone uses second voice input information, which includes multiple words. The second word is any one of these multiple words, and it is the same word as the first word. In this embodiment, "the second word and the first word are the same word" can be understood as meaning that their word lengths are similar or identical. The standard for judging similarity or identicalness can be obtained by calculating their similarity. For example, if a user is memorizing the word "abandon," and then half an hour later is memorizing the word "project," since "abandon" and "project" have the same word length, the smart earphone considers them comparable, facilitating subsequent analysis of memorization efficiency based on the number of times each word has been memorized.

[0051] S150. Determine the trend of memory frequency based on the number of times the first word and the number of times the second word are memorized.

[0052] Specifically, after the smart earphones acquire the number of times the first word and the second word are memorized, they determine the trend of the user's memorization frequency based on these numbers. This trend reflects the user's memorization efficiency over a period of time. For example, if the number of times a user memorizes the same word gradually increases within a limited time, resulting in an upward trend, the smart earphones will conclude that the user's memorization efficiency has decreased.

[0053] In one possible implementation, the trend of memory count changes is determined based on the memory counts of the first word and the second word. Specifically, this includes: inputting the memory counts of the first word and the second word, as well as the first time period and the second time period, into the ARIMA model to obtain a memory curve. The ARIMA model pre-stores the historical memory counts of multiple words; and analyzing the memory curve to determine the trend of memory count changes.

[0054] Specifically, the memory counts of the first and second words, along with the first and second time periods, are input into the ARIMA model. Here, the memory counts of the two words and the two time periods are used as input data. The ARIMA model generates a memory curve based on the input data—the memory counts of the two words in the two time periods—and pre-stored historical memory counts. This means that the ARIMA model considers not only the current input data but also historical data, allowing for a more comprehensive understanding of data trends. The ARIMA model includes three parameters: Autoregressive (AR), Differential (I), and Moving Average (MA). Its core idea is to transform time series data into a stationary series and then use the past and present values ​​of this series to predict the future. For example, if a time series shows a clear upward trend, it can be transformed into a stationary series through differencing and then predicted using the ARMA model.

[0055] For example, suppose we have two words, "apple" and "share," and we count the number of times they are remembered at 9 AM and 10 AM. Next, we input this data into the ARIMA model to obtain a memory curve. This curve shows how the number of times these two words are remembered changes over time. If the curve shows an upward or downward trend, it indicates that the number of times the words are remembered is increasing or decreasing, thus revealing the trend of memory frequency.

[0056] S160. If it is determined that the trend of memory count changes is upward, then the user will be prompted that the memory efficiency is decreasing according to the preset method.

[0057] Specifically, when the smart earphones determine that the number of memorization attempts is trending upwards, they will alert the user to a decline in memory efficiency based on a preset method. This preset method is a user-defined reminder, which may include playing audio data or vibration to indicate a decrease in memory efficiency and encourage the user to rest before attempting memorization again. Therefore, by monitoring the user's vocabulary memorization process in real time, the system can analyze the user's memory efficiency based on the trend of the number of attempts, allowing the user to accurately identify a decline in their memory efficiency.

[0058] In one possible implementation, the preset method is to play a prompt message to the user, the prompt message including the memory duration, the prompt message being used to remind the user to take a break.

[0059] Specifically, the above content is a specific preset method provided in the embodiments of this application. When the smart earphone determines that the user's memory efficiency has decreased, it will play a prompt message to the user, including the duration of memory work. For example, the prompt message could be: "User XX has worked for XX hours. Your memory efficiency is detected to be poor. We suggest you take a short break." Thus, the prompt message can remind the user to reasonably arrange rest time during the learning process to avoid over-fatigue. This rest reminder is beneficial to protecting the user's physical health and improving learning efficiency. Through timely feedback and rest reminders, users can better adjust their learning strategies and improve learning efficiency. This prompt message can also serve as an incentive, encouraging users to maintain their enthusiasm and motivation during the learning process. By playing prompt messages, the user experience can be enhanced, making the user feel more comfortable and humanized.

[0060] In one possible implementation, in response to a user's word playback request, the user plays the word's audio information according to the request; the user's voice input information is obtained, including first voice input information and second voice input information; the similarity between the word's audio information and the voice input information is calculated; if the similarity is confirmed to be less than a preset similarity threshold, voice correction information is played to correct the user's voice input information.

[0061] Specifically, when a user is memorizing vocabulary using smart earphones, the earphones first respond to the user's vocabulary playback request. This request instructs the earphones to recognize the vocabulary the user needs to memorize, allowing them to play the relevant words. Next, the earphones play the vocabulary audio information according to the request and simultaneously acquire the user's voice input. Then, the earphones calculate the similarity between the vocabulary audio information and the voice input. The specific methods for calculating similarity include, but are not limited to, cosine similarity and Hamming similarity, which are not specified here. When the earphones determine that the similarity is less than a preset similarity threshold, they play voice correction information to correct the user's voice input. When the similarity is greater than or equal to the preset similarity threshold, it indicates that the user's pronunciation is correct. In this embodiment, the preset similarity threshold is determined based on the standard pronunciation of the vocabulary. Users often mispronounce words during the memorization process, leading to errors. By calculating the similarity between the vocabulary audio information and the voice input, and determining whether to play voice correction information based on the similarity, the system helps users better master the pronunciation and spelling of vocabulary, allowing for adaptive adjustments based on the user's actual learning progress and making learning more personalized. By playing voice correction information, users' pronunciation and spelling errors can be corrected in a timely manner, thereby improving learning efficiency and avoiding subsequent learning problems caused by incorrect pronunciation and spelling. By comparing the similarity between vocabulary voice information and user voice input information, learning can be guided more intelligently, enhancing the learning experience and learning outcomes.

[0062] In one possible implementation, if it is determined that the trend of memory count changes is downward, incentive information is played to the user to prompt the user to maintain the current memory efficiency.

[0063] Specifically, if the trend of memory recall frequency is determined to be downward, indicating that the user's memory efficiency is high, then incentive messages are displayed to the user, prompting them to maintain the current memory efficiency. This incentive can enhance the user's confidence and motivation, encouraging them to continue their efforts. Timely feedback and incentives can increase user engagement with the device, improving their learning enthusiasm and participation. Continuous incentives and feedback can help users develop good learning and memory habits, which has a positive impact on their long-term learning and development. Users can adjust their learning strategies based on the incentive messages to better cope with changes in memory efficiency and improve learning outcomes.

[0064] This application also provides a learning monitoring device for smart headphones, referring to... Figure 2 , Figure 2This is a schematic diagram of a learning monitoring device applied to a smart earphone, provided in an embodiment of this application. The learning monitoring device is a smart earphone, which includes an acquisition module 21 and a processing module 22. The acquisition module 21 is used to acquire first voice input information, which is the user's voice input information in a first time period. The processing module 22 is used to obtain the number of times a first word is memorized based on the first voice input information. The acquisition module 21 is also used to acquire second voice input information, which is the user's voice input information in a second time period, which is later than the first time period. The processing module 22 is also used to obtain the number of times a second word is memorized based on the second voice input information, where the first word and the second word are the same word. The processing module 22 is also used to determine the trend of the number of memorizations based on the number of times the first word and the number of times the second word are memorized. If the trend of the number of memorizations is determined to be upward, the processing module 22 will prompt the user that the memorization efficiency has decreased according to a preset method.

[0065] In one possible implementation, acquiring the first voice input information specifically includes: the acquisition module 21 receiving audio data input by the user, the audio data being the audio data of the user reciting vocabulary; and the processing module 22 preprocessing the audio data to obtain the first voice input information, the preprocessing including data cleaning, data denoising, and data normalization.

[0066] In one possible implementation, the processing module 22 obtains the number of times the first word is memorized based on the first voice input information, specifically including: the processing module 22 performs voice recognition on the first voice input information to obtain corresponding text information, the text information including multiple words, and the first word being any one of the multiple words; the processing module 22 performs word frequency statistics on the text information to obtain the number of times the first word appears; the processing module 22 determines the number of times the first word is memorized based on the number of times the first word appears.

[0067] In one possible implementation, the processing module 22 determines the trend of memory count changes based on the memory counts of the first word and the memory counts of the second word. Specifically, the processing module 22 inputs the memory counts of the first word and the second word, as well as the first time period and the second time period, into the ARIMA model to obtain a memory curve. The ARIMA model pre-stores the historical memory counts of multiple words. The processing module 22 analyzes the memory curve to determine the trend of memory count changes.

[0068] In one possible implementation, the preset method is to play a prompt message to the user, the prompt message including the memory duration, the prompt message being used to remind the user to take a break.

[0069] In one possible implementation, the acquisition module 21 responds to the user's word playback request; the processing module 22 plays the word speech information according to the word playback request; the acquisition module 21 acquires the user's speech input information, which includes first speech input information and second speech input information; the processing module 22 calculates the similarity between the word speech information and the speech input information; if the processing module 22 confirms that the similarity is less than a preset similarity threshold, it plays speech correction information to correct the user's speech input information.

[0070] In one possible implementation, if the processing module 22 determines that the trend of memory count changes is downward, it plays incentive information to the user to remind the user to maintain the current memory efficiency.

[0071] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0072] This application also provides an electronic device, with reference to... Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 31, at least one network interface 34, a user interface 33, a memory 35, and at least one communication bus 32.

[0073] The communication bus 32 is used to enable communication between these components.

[0074] The user interface 33 may include a display screen and a camera. Optionally, the user interface 33 may also include a standard wired interface and a wireless interface.

[0075] The network interface 34 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0076] The processor 31 may include one or more processing cores. The processor 31 connects to various parts of the server via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in the memory 35, and calling data stored in the memory 35 to perform various server functions and process data. Optionally, the processor 31 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 31 and may be implemented as a separate chip.

[0077] The memory 35 may include random access memory (RAM) or read-only memory. Optionally, the memory 35 may include a non-transitory computer-readable storage medium. The memory 35 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 35 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 35 may also be at least one storage device located remotely from the aforementioned processor 31. Figure 3 As shown, the memory 35, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a learning monitoring method applied to smart headphones.

[0078] exist Figure 3In the electronic device shown, the user interface 33 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 31 can be used to call an application stored in the memory 35 for a learning monitoring method for smart headphones. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0079] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0080] This application also provides a computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0081] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0083] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0084] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0085] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0086] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A learning monitoring method applied to smart headphones, characterized in that, The method includes: Obtain first voice input information, which is the user's voice input information in a first time period; Based on the first voice input information, the number of times the first word is memorized is obtained; Obtain second voice input information, which is the user's voice input information in a second time period, which is later than the first time period; Based on the second voice input information, the number of times the second word is memorized is obtained, and the first word and the second word are the same word; Based on the number of times the first word was memorized and the number of times the second word was memorized, determine the trend of the number of memorizations. If it is determined that the trend of the number of memorization attempts is upward, the user will be prompted that their memorization efficiency is declining according to a preset method.

2. The learning monitoring method for smart headphones according to claim 1, characterized in that, The acquisition of the first voice input information specifically includes: Receive audio data input by the user, wherein the audio data is the audio data of the user when memorizing vocabulary; The audio data is preprocessed to obtain the first voice input information. The preprocessing includes data cleaning, data denoising, and data normalization.

3. The learning monitoring method for smart headphones according to claim 1, characterized in that, The step of obtaining the number of times the first word is memorized based on the first voice input information specifically includes: The first voice input information is subjected to speech recognition to obtain the corresponding text information, the text information including multiple words, and the first word is any one of the multiple words; The frequency of the first word is obtained by performing word frequency statistics on the text information. The number of times the first word appears is used to determine the number of times the first word is memorized.

4. The learning monitoring method for smart headphones according to claim 1, characterized in that, The step of determining the trend of memory frequency changes based on the memory frequency of the first vocabulary word and the memory frequency of the second vocabulary word specifically includes: The memory counts of the first word and the second word, as well as the first time period and the second time period, are all input into the ARIMA model to obtain the memory curve. The ARIMA model has pre-stored the historical memory counts of multiple words. The memory curve is analyzed to determine the trend of the number of times the memory is recalled.

5. The learning monitoring method for smart headphones according to claim 1, characterized in that, The preset method is to play a prompt message to the user, the prompt message including the memory duration, the prompt message being used to remind the user to take a break.

6. The learning monitoring method for smart headphones according to claim 1, characterized in that, The method further includes: Responding to the user's word playback request; Play the vocabulary audio information according to the vocabulary playback request; Acquire user's voice input information, the voice input information including the first voice input information and the second voice input information; Calculate the similarity between the lexical speech information and the speech input information; If the similarity is confirmed to be less than a preset similarity threshold, voice correction information is played to correct the user's voice input.

7. The learning monitoring method for smart headphones according to claim 1, characterized in that, The method further includes: If it is determined that the trend of the number of times the memory is changed is downward, then incentive information is played to the user to prompt the user to maintain the current memory efficiency.

8. A learning monitoring device for use in smart headphones, characterized in that, The learning monitoring device includes an acquisition module (21) and a processing module (22), wherein, The acquisition module (21) is used to acquire first voice input information, which is the user's voice input information in a first time period; The processing module (22) is used to obtain the number of times the first word is memorized based on the first voice input information; The acquisition module (21) is also used to acquire second voice input information, which is the user's voice input information in a second time period, which is later than the first time period; The processing module (22) is also used to obtain the number of times the second word is memorized based on the second voice input information, wherein the first word and the second word are the same word; The processing module (22) is also used to determine the trend of memory count changes based on the memory counts of the first word and the memory counts of the second word; The processing module (22) is also used to prompt the user that the memory efficiency has decreased if it is determined that the trend of the number of times the memory is changing is upward.

9. An electronic device, characterized in that, The electronic device includes a processor (31), a memory (35), a user interface (33), and a network interface (34). The memory (35) is used to store instructions. The user interface (33) and the network interface (34) are both used to communicate with other devices. The processor (31) is used to execute the instructions stored in the memory (35) to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

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