Intel Corporation

Sebastian Czyryba

Tobias Bocklet

Joachim Hofer

Georg Stemmer

Josef G Bauer

David Pearce

Kuba Lopatka

Lukasz Kurylo

Tomasz Noczynski

Techniques related to keyphrase detection for applications such as wake on voice are disclosed herein. Such techniques may have high accuracy by using scores of phone positions in triphones to select which triphones to use with a rejection model, using context-related phones for the rejection model, adding silence before keyphrase sounds for a keyphrase model, or any combination of these.

Method and system of high accuracy keyphrase detection for low resource devices

An example apparatus for suppression of key phrase detection includes an audio receiver to receive generated audio from a loopback endpoint and captured audio from a microphone. The apparatus includes a self-trigger detector to detect a key phrase in the generated audio. The apparatus also includes a detection suppressor to suppress detection of the detected key phrase in the captured audio at a second key detector for a predetermined time in response to detecting the key phrase in the generated audio.

Suppressing key phrase detection in generated audio using self-trigger detector

Technologies for identifying sounds are disclosed. A sound identification device may capture sound data, and split the sound data into frames. The sound identification device may then determine an acoustic feature vector for each frame, and determine parameters based on how each acoustic feature varies over the duration of time corresponding to the frames. The sound identification device may then determine if the sound matches a pre-defined sound based on the parameters. In one embodiment, the sound identification device may be a baby monitor, and the pre-defined sound may be a baby crying.

Technologies for robust crying detection using temporal characteristics of acoustic features

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Mragowo, Poland

Sebastian Czyryba