A language proficiency analyzer automatically evaluates a person's language proficiency by analyzing that person's oral communications with another person. The analyzer first enhances the quality of an audio recording of a conversation between the two people using a neural network that automatically detects loss features in the audio and adds those loss features back into the audio. The analyzer then performs a textual and audio analysis on the improved audio. Through textual analysis, the analyzer uses a multi-attention network to determine how focused one person is on the other and/or how pleased one person is with the other. Through audio analysis, the analyzer uses a neural network to determine how well one person pronounced words during the conversation.

A language proficiency analyzer automatically evaluates a person's language proficiency by analyzing that person's oral communications with another person. The analyzer first enhances the quality of an audio recording of a conversation between the two people using a neural network that automatically detects loss features in the audio and adds those loss features back into the audio. The analyzer then performs a textual and audio analysis on the improved audio. Through textual analysis, the analyzer uses a multi-attention network to determine how focused one person is on the other and/or how pleased one person is with the other. Through audio analysis, the analyzer uses a neural network to determine how well one person pronounced words during the conversation.

A set of machines functions as a language teaching lab. Configured by suitable hardware, software, accessories, or any suitable combination thereof, such a language teaching lab accesses multiple sources and types of data, such as video streams, audio streams, thermal imaging data, eye tracker data, breath anemometer data, biosensor data, accelerometer data, depth sensor data, or any suitable combination thereof. From the accessed data, the language teaching lab detects that the user is pronouncing, for example, a word, a phrase, or a sentence, and then causes presentation of a reference pronunciation of that word, phrase, or sentence. Other apparatus, systems, and methods are also disclosed.

A set of machines functions as a language teaching lab. Configured by suitable hardware, software, accessories, or any suitable combination thereof, such a language teaching lab accesses multiple sources and types of data, such as video streams, audio streams, thermal imaging data, eye tracker data, breath anemometer data, biosensor data, accelerometer data, depth sensor data, or any suitable combination thereof. From the accessed data, the language teaching lab detects that the user is pronouncing, for example, a word, a phrase, or a sentence, and then causes presentation of a reference pronunciation of that word, phrase, or sentence. Other apparatus, systems, and methods are also disclosed.

The embodiments disclose a method including using an on-demand roleplay network with internet connectivity to role playing study materials and real-time role playing sessions, connecting an on-demand roleplay application installed on role playing participant digital devices with the on-demand roleplay network to facilitate role playing partners real-time practice sessions, incorporating an artificial intelligence communicator coach analyzer with the on-demand roleplay network to provide role playing participants with nonverbal communication analysis and coaching, and providing role playing participants with a means to rate live practice sessions including the nonverbal communication skills.

The embodiments disclose a method including using an on-demand roleplay network with internet connectivity to role playing study materials and real-time role playing sessions, connecting an on-demand roleplay application installed on role playing participant digital devices with the on-demand roleplay network to facilitate role playing partners real-time practice sessions, incorporating an artificial intelligence communicator coach analyzer with the on-demand roleplay network to provide role playing participants with nonverbal communication analysis and coaching, and providing role playing participants with a means to rate live practice sessions including the nonverbal communication skills.

Described herein is a software-based simulator to provide speech and hearing students the ability to perform hearing screening training using a realistic interface similar to devices used to perform these tests, which provides results for Otoscopy, Tympanometry, and Audiometry testing, and assesses the students responses for correctness.

Provided are various mechanisms and processes for language acquisition using socio-neurocognitive techniques.

Systems and methods of presenting interrupting content during human speech are disclosed. The proposed systems offer improved duplex communications in conversational AI platforms. In some embodiments, the system receives speech data and evaluates the data using linguistic models. If the linguistic models detect indications of linguistic irregularities such as mispronunciation, a smart feedback assistant can determine that the system should interrupt the speaker in near-real-time and provide feedback regarding their pronunciation. In addition, conversational irregularities may also be detected, causing the smart feedback assistant to interrupt with presentation of moderating guidance. In some cases, emotion models may also be utilized to detect emotional states based on the speaker's voice in order to offer near-immediate feedback. Users can also customize the manner and occasions in which they are interrupted.

A training device changes feedback formant frequencies which are formant frequencies of a picked-up speech signal, applies a lowpass filter, converts the picked-up speech signal, adds high-pass noise to the converted speech signal, feeds back the converted speech signal with the high-pass noise added to a subject, calculates a compensatory response vector by using pickup formant frequencies which are formant frequencies of a speech signal acquired by picking up an utterance made by the subject while feeding back a speech signal that has been converted with change of the feedback formant frequencies to the subject, and pickup formant frequencies which are formant frequencies of a speech signal acquired by picking up an utterance made by the subject while feeding back a speech signal that has been converted without change of the feedback formant frequencies to the subject, and determines an evaluation based on the compensatory response vector and a correct compensatory response vector.