Disclosed is a method of phonetically encoding a text document. The method comprises providing, for a current word in the text document, a phonetically equivalent encoded word comprising one or more syllables, each syllable comprising a sequence of phonemes from a predetermined phoneme set, the sequence being phonetically equivalent to the corresponding syllable in the current word, and adding the phonetically equivalent encoded word or the current word at a current position in the phonetically encoded document, Each phoneme in the phoneme set is associated with a base grapheme that is pronounced as the phoneme in one or more English words.

Disclosed is a method of phonetically encoding a text document. The method comprises providing, for a current word in the text document, a phonetically equivalent encoded word comprising one or more syllables, each syllable comprising a sequence of phonemes from a predetermined phoneme set, the sequence being phonetically equivalent to the corresponding syllable in the current word, and adding the phonetically equivalent encoded word or the current word at a current position in the phonetically encoded document, Each phoneme in the phoneme set is associated with a base grapheme that is pronounced as the phoneme in one or more English words.

Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.

A method in which a server recommends a word to a user according to the present specification, includes receiving training data from a network and training an AI model by using the training data; inputting (1) a user vector and (2) a word vector to the AI model, and generating (1) a user embedding vector and (2) a word embedding vector for determining whether the user knows a word related to the word vector, on the basis of the trained AI model; inputting (1) the user embedding vector and (2) the word embedding vector to a function for determining whether the user knows a word related to the word vector; and outputting a result value for predicting whether the user knows a word related to the word vector from the function.

A method in which a server recommends a word to a user according to the present specification, includes receiving training data from a network and training an AI model by using the training data; inputting (1) a user vector and (2) a word vector to the AI model, and generating (1) a user embedding vector and (2) a word embedding vector for determining whether the user knows a word related to the word vector, on the basis of the trained AI model; inputting (1) the user embedding vector and (2) the word embedding vector to a function for determining whether the user knows a word related to the word vector; and outputting a result value for predicting whether the user knows a word related to the word vector from the function.

According to an aspect of the present specification, a method in which a terminal recommends a word to a user includes: receiving recommended word information from a server, wherein the recommended word information includes word information that the user is predicted not to know in an AI (Artificial Intelligence) model of the user on the basis of training data of the user; displaying a first window including the word information on the basis of the recommended word information; receiving operation of dragging the first window from the user; displaying an icon representing whether to add the word information to a vocabulary list of the terminal on the basis of a direction of the dragging operation; and including the word information in the vocabulary on the basis of the direction of the dragging operation.

According to an aspect of the present specification, a method in which a terminal recommends a word to a user includes: receiving recommended word information from a server, wherein the recommended word information includes word information that the user is predicted not to know in an AI (Artificial Intelligence) model of the user on the basis of training data of the user; displaying a first window including the word information on the basis of the recommended word information; receiving operation of dragging the first window from the user; displaying an icon representing whether to add the word information to a vocabulary list of the terminal on the basis of a direction of the dragging operation; and including the word information in the vocabulary on the basis of the direction of the dragging operation.

The present disclosure provides a system for hosting an online platform with multiple functionalities, separated into a plurality of interfaces, but all hosted within an integrated system to increase the immersion of a user in the learning experience. Multimedia content streaming, educational course, history, and tracking, and business management functions are provided on the various interfaces that quickly educate a user about a given industry. The platform is industry agnostic but can also be provided with specific functionalities such as competitive tournament hosting for the e-sports industry. Also provided herein is a method of translating an educational lecture from a first language into a plurality of second languages.

Systems and methods for evaluating and improving reading skills in real time are disclosed. The method may include receiving a user selection of an electronic book in an application executing on a user device associated with a first user, opening the user selection of the electronic book in the application, initiating a video call or an audio call with a second user in the application, receiving from the first user one or more first spoken words associated with a first reading content of the electronic book, transcribing the one or more first spoken words to first text via the application, determining if the first text matches the first reading content of the electronic book, and transmitting the determination of whether the first text matches the first reading content of the electronic book to the first user and the second user.

The present invention provides a system for determining a language proficiency of a user in an evaluated language. A machine learning engine may be trained using audio file variables from a plurality of audio files and human generated scores for a comprehensibility, accentedness and intelligibility for each audio file. The system may receive an audio file from a user and determine a plurality of audio file variables from the audio file. The system may apply the audio file variables to the machine learning engine to determine a comprehensibility, an accentedness and an intelligibility score for the user. The system may determine one or more projects and/or classes for the user based on the user's comprehensibility score, accentedness score and/or intelligibility score.