International Business Machines Corporation

Ryan Young

Ching-Chun Liu

Ting-Chieh Yu

Yu-Siang Chen

Cheng-Fang Lin

Yu-siang Chen

A method, computer program product, and computer system for public speaking guidance is provided. A processor retrieves speaker data regarding a speech made by a user. A processor separates the speaker data into one or more speaker modalities. A processor extracts one or more speaker features from the speaker data for the one or more speaker modalities. A processor generates a performance classification based on the one or more speaker features. A processor sends to the user guidance regarding the speech based on the performance classification.

Multimodal neural network for public speaking guidance

A computer-implemented method according to one embodiment includes running an initial network on a plurality of images to detect actors pictured therein and body joints of the detected actors. The method further includes running fully-connected networks in parallel, one fully-connected network for each of the detected actors, to reconstruct complete three-dimensional poses of the actors. Sequential model fitting is performed on the plurality of images. The sequential model fitting is based on results of running the initial network and the fully-connected networks. The method further includes determining, based on the sequential model fitting, a locational position for a camera in which the camera has a view of a possible point of collision of two or more of the actors. The camera is instructed to be positioned in the locational position.

Determination of a locational position for a camera to capture a collision of two or more actors

A live stream, that includes a video stream and an audio stream, of a presenter is monitored. The live stream is attended by an audience that includes one or more audience members. One or more stream content features of the live stream at a first window of time is transmitted to a multimodal machine learning model. One or more audience content features of the audience at the first window of time is transferred to the multimodal model. One or more feature results, based on the stream content features and based on the audience content features, of the first window of time is obtained from the multimodal model. The feature results are sent to an auditory machine learning model. A first audio signal from the auditory machine learning model is received. An augmented stream of the first window of time is generated based on the first audio signal.

Audio customization in streaming environment

Computer technology for sending an image a device to be authenticated. The image is designed to be classified to a first category by an image classifier, and the first category is different from a nature category of the image. A response message can be received from the device. The response message indicates a second category of the image determined by the device. Then, the device is determined to be an authorized device in response to the second category being consistent with the first category.

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Taipei, Taiwan

Ryan Young