Cisco Technology, Inc.

Eric Yi-Hua Chen

Wei-Lien Hsu

Bjørn Winsvold

Pi-Hsin Liu

Keith Griffin

Managing noise during an online conference session includes obtaining audio data from an endpoint participating in an online conference session. The audio data is derived from audio captured at the endpoint that includes musical sounds. The audio data is processed to identify a portion of the audio data in which a decibel level of the musical sounds is stable for a period of time. Non-musical noise present, if any, in the audio data with the musical sounds is identified and the non-musical noise is attenuated from the audio data to generate noise-reduced musical audio data. The noise-reduced musical audio data is transmitted for play out at one or more other endpoints participating in the online conference session.

Noise management during an online conference session

A system and method for authenticating a user via a single voice audio input is disclosed. The method includes obtaining voice audio from a user, converting the voice audio to text, comparing at least a portion of the text to a database, determining whether a user profile exists in the database based on the comparing, the user profile including a voiceprint, in response to determining that the user profile exists in the database, analyzing the voice audio against the voiceprint of the user profile; and authenticating the user based on the voice audio substantially matching the voiceprint of the user profile.

Single input voice authentication

A system and method for training a virtual assistant to recognize and learn new context for known terms is presented. The method includes receiving a natural language input, corresponding to at least one of a desired intent and a desired entity, at a natural language processor. The method involves scoring known intents based on the natural language input to generate an intent confidence score for each known intent, and scoring known entities based on the natural language input to generate an entity confidence score for each known entity. The method involves comparing the intent confidence scores and entity confidence scores to a threshold value, and determining that the natural language input does not correspond to at least one of the known intents and the known entities based on the comparing. Finally, at least one of a new intent and a new entity are determined based on the natural language input.

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Irvine, CA, United States of America

Eric Yi-Hua Chen