Abstract: The present disclosure generally relates to systems and methods for generating and evaluating a transcript summary based on a transcript of an active meeting. A summary evaluation system may evaluate a summary based on both completeness and accuracy. The summary evaluation system may generate, via a machine learning model, questions and correct answers based on a first representation of the transcript (e.g., the transcript summary). The summary evaluation system may determine possible answers to the questions based on a second representation of the transcript (e.g., transcript). Depending on a comparison between the answers, a completeness score of the summary is generated. In some embodiments, the first representation of the transcript may be the transcript, the second representation of the transcript may be the generated transcript summary, and the score may be an accuracy score. Regeneration of the summary may be prompted if the score is below a threshold.

Abstract: A system may receive sound information and generate an inference embedding using the sound information. The system may additionally receive a set of speaker embeddings, which may represent voice information for a set of speakers. The system may compare the inference embedding to the set of speaker embeddings to generate a result. The system may determine, based on the result, a speaker identity match rating for each speaker embedding in the set of speaker embeddings. The system may identify a speaker associated with a speaker embedding of the set of speaker embeddings having the highest speaker identity match rating as an active speaker.

Abstract: Examples herein provide an approach to enhance an audio mixture of a teleconference application by switching between noise suppression modes using a single model. Specifically, a machine learning (ML) model may be configured to, in response to receiving an audio mixture representation as input, suppress either a background noise of the audio mixture or suppress all noise of the audio mixture except a user's voice. In some examples, the ML model may be trained on speech and background noise training data during a training phase. In addition, the ML model may be trained on a user's voice during an enrollment phase. In addition, during an inference phase, the ML model may enhance the audio mixture by suppressing a portion of the audio mixture.

Abstract: A system, method, and computer program product that includes a processor(s) standardizing data from sources that includes details for materials for integration into a custom-designed item. The processor(s) store the standardized data in a consolidated data set one or more databases accessible to an API. The processor(s) trigger the API, based on a selected in a graphical user interface communicatively coupled to the API, to query the one or more databases to obtain select data from the consolidated data set about a portion of the materials that could potentially fulfill a parameter for the custom-designed item. The processor(s) optimize the select data, to determine one or more physical layouts for the portion of the materials in the custom-designed item. The processor(s) render and display a two-dimensional model of the custom-designed object with the one or more physical layouts implemented into the custom-designed object.