Abstract: The current disclosure relates to systems and methods for wakeword or keyword detection in Virtual Personal Assistants (VPAs). In particular, systems and methods are provided for wakeword detection using deep neural networks including a parametric pooling layer, wherein the parametric pooling layer includes trainable parameters, enabling the layer to learn to distinguish between informative feature vectors and non-informative/noisy feature vectors extracted from a variable length acoustic signal. In one example, a parametric pooling layer may aggregate a variable length feature map, comprising a plurality of feature vectors extracted from an acoustic signal, into an embedding vector of pre-determined length, by weighting each of the plurality of feature vectors based on one or more learned parameters in a parametric pooling layer, and aggregating the plurality of weighted feature vectors into the embedding vector.

Abstract: Embodiments are disclosed for providing an interface with a personalized virtual personal assistant (VPA) via a computing system. The example method comprises assigning a plurality of virtual personal assistant (VPA) instances to a plurality of users, each VPA instance of the plurality of VPA instances operating concurrently. For example, assigning the plurality of VPA instances to the plurality of users may include retrieving a personalized VPA configuration for each user of the plurality of users based on a plurality of audio samples, each of the plurality of audio samples corresponding to a user of the plurality of users.

Abstract: Embodiments are disclosed for providing an interface with a personalized virtual personal assistant (VPA) via a computing system. The example method comprises assigning a plurality of virtual personal assistant (VPA) instances to a plurality of users, each VPA instance of the plurality of VPA instances operating concurrently. For example, assigning the plurality of VPA instances to the plurality of users may include retrieving a personalized VPA configuration for each user of the plurality of users based on a plurality of audio samples, each of the plurality of audio samples corresponding to a user of the plurality of users.

Abstract: The current disclosure relates to systems and methods for wakeword or keyword detection in Virtual Personal Assistants (VPAs). In particular, systems and methods are provided for wakeword detection using deep neural networks including a parametric pooling layer, wherein the parametric pooling layer includes trainable parameters, enabling the layer to learn to distinguish between informative feature vectors and non-informative/noisy feature vectors extracted from a variable length acoustic signal. In one example, a parametric pooling layer may aggregate a variable length feature map, comprising a plurality of feature vectors extracted from an acoustic signal, into an embedding vector of pre-determined length, by weighting each of the plurality of feature vectors based on one or more learned parameters in a parametric pooling layer, and aggregating the plurality of weighted feature vectors into the embedding vector.