Difference between revisions of "MULTI-TASK LEARNING FOR PERSONALIZED KEYWORD SPOTTING: abstract simplified (18153932)"

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Systems and techniques are described for processing audio data using personalized keyword spotting through multi-task learning (PK-MTL). This involves obtaining an audio sample and generating representations of both a keyword and a speaker based on the sample. The speaker is associated with the keyword. A similarity score is calculated based on a reference representation and either the keyword representation or the speaker representation. This score is then analyzed against a threshold to determine if the audio sample includes the target keyword.
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Systems and techniques are described for processing audio data, specifically for personalized keyword spotting through multi-task learning (PK-MTL). The process involves obtaining an audio sample and generating representations of both a keyword and a speaker based on the sample. These representations are then used to determine a similarity score against a reference representation, which is associated with the keyword and/or the speaker. Based on this similarity score and a threshold, a keyword spotting (KWS) output is generated to determine if the audio sample includes the target keyword.

Latest revision as of 16:20, 1 October 2023

Systems and techniques are described for processing audio data, specifically for personalized keyword spotting through multi-task learning (PK-MTL). The process involves obtaining an audio sample and generating representations of both a keyword and a speaker based on the sample. These representations are then used to determine a similarity score against a reference representation, which is associated with the keyword and/or the speaker. Based on this similarity score and a threshold, a keyword spotting (KWS) output is generated to determine if the audio sample includes the target keyword.