A conference system transmits a first graphical output to display an indicator of a conference item on a display of a client device. Prior to the client device joining the conference, the conference system receives a request to view the conference item. The conference system transmits a second graphical output to the client device to display the conference item.

A conference system transmits a first graphical output to display an indicator of a conference item on a display of a client device. Prior to the client device joining the conference, the conference system receives a request to view the conference item. The conference system transmits a second graphical output to the client device to display the conference item.

A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Methods, systems and processor-readable media for providing instant/real-time voice alerts automatically to remote electronic devices. An activity can be detected utilizing one or more sensors. A text message indicative of the activity can be generated and converted into a digitized voice alert. The activity can also be a live utterance (e.g., a live announcement), which can then be instantly converted into a digitized voice alert for automatic delivery in a selected series of languages following the base language (e.g., English). The combined digitized voice alert can then be instantly transmitted through a network for broadcast of consecutive alerts (e.g., English followed by Spanish followed by Vietnamese, etc.) to one or more remote electronic devices that communicate with the network for an automatic audio announcement of the digitized voice alert through the one or more remote electronic devices.

Methods, systems and processor-readable media for providing instant/real-time voice alerts automatically to remote electronic devices. An activity can be detected utilizing one or more sensors. A text message indicative of the activity can be generated and converted into a digitized voice alert. The activity can also be a live utterance (e.g., a live announcement), which can then be instantly converted into a digitized voice alert for automatic delivery in a selected series of languages following the base language (e.g., English). The combined digitized voice alert can then be instantly transmitted through a network for broadcast of consecutive alerts (e.g., English followed by Spanish followed by Vietnamese, etc.) to one or more remote electronic devices that communicate with the network for an automatic audio announcement of the digitized voice alert through the one or more remote electronic devices.

A system and method for biofeedback-based control of sexual stimulation devices involving receiving biometric data, analyzing the biometric data to detect changes in the physiology of a person, and generating control signals based on the changes. In some embodiments, the analyses of the biometric data are performed by machine learning algorithms which may be trained on associations between biometric data of a user, indications of the user's state of arousal, and the state of operation of a sexual stimulation device. In some embodiments, machine learning algorithms are used to make the associations. In some embodiments, biofeedback-based controls may be incorporated into systems of controls comprising thought-based controls and/or voice-based controls.

A system and method for adaptive speech and biofeedback control of sexual stimulation devices. In an embodiment, the system and method involve receiving audio from a microphone, processing the audio through an automated speech detection engine to detect speech within the audio, matching the speech to a control command for a sexual stimulation device, generating a control signal for the sexual stimulation device based on the control command, receiving biometric data from a biometric sensor, and adjusting the control signals based on the biometric data before outputting the adjusted control signal for use in operating the sexual stimulation device. In some embodiments, the adjustment to the control signal is made automatically by a machine learning algorithm using the command and biometric data as inputs.

An information processing apparatus includes a data collection section, a relation learning section, and a map generation section. The data collection section collects a feature quantity of a physical movement of a user and situation information indicating a situation of the user when the physical movement is made. The relation learning section creates a learned model for classification of the feature quantity of the physical movement according to the situation information, by learning a relation between the feature quantity of the physical movement and the situation information. The map generation section generates, on the basis of the learned model, a map that is capable of associating the situation information with the feature quantity of the physical movement.

Audio distortion compensation methods to improve accuracy and efficiency of audio content identification are described. The method is also applicable to speech recognition. Methods to detect the interference from speakers and sources, and distortion to audio from environment and devices, are discussed. Additional methods to detect distortion to the content after performing search and correlation are illustrated. The causes of actual distortion at each client are measured and registered and learnt to generate rules for determining likely distortion and interference sources. The learnt rules are applied at the client, and likely distortions that are detected are compensated or heavily distorted sections are ignored at audio level or signature and feature level based on compute resources available. Further methods to subtract the likely distortions in the query at both audio level and after processing at signature and feature level are described.