Qualcomm Incorporated

Seunghan Yang

Byeonggeun Kim

Simyung Chang

Inseop Chung

Jae-won Choi

Kyu Woong Hwang

Sungrack Yun

Juntae Lee

Janghoon Cho

Hyunsin Park

Hanul Kim

Hyoungwoo Park

Systems and techniques are provided for processing audio data. For example, a dummy prototypical network may be used to perform few-shot open-set keyword spotting (FSOS-KWS). A process can include determining one or more prototype representations based on a plurality of support samples associated with one or more classes. Each prototype representation may be associated with one of the class(es). A dummy prototype representation can be determined in a same learned metric space as the prototype representations. One or more distance metrics can be determined for each query sample of one or more query samples. The distance metrics may be based on the prototype representations and the dummy prototype representation. Each query sample can be classified based on the distance metrics. Each query sample may be classified into one of the class(es) associated with the prototype representations or into an open-set class associated with the dummy prototype representation.

Dummy prototypical networks for few-shot open-set keyword spotting

Systems and techniques are provided for processing audio data. For example, the systems and techniques can be used for personalized keyword spotting through multi-task learning (PK-MTL). A process can include obtaining an audio sample, generating a representation of a keyword based on the audio sample, and generating a representation of a speaker based on the audio sample. The speaker can be associated with the keyword. A first similarity score can be determined based on a reference representation and one or more of the representation of the keyword and a representation of the speaker. The reference representation can be associated with one or more of the keyword and the speaker. A keyword spotting (KWS) output can be generated based on analyzing the first similarity score against at least a first threshold, wherein the KWS output accepts or rejects the audio sample as including a target keyword.

Multi-task learning for personalized keyword spotting

Techniques and apparatus for training a neural network to classify audio into one of a plurality of categories and using such a trained neural network. An example method generally includes receiving a data set including a plurality of audio samples. A relaxed feature-normalized data set is generated by normalizing each audio sample of the plurality of audio samples. A neural network is trained to classify audio into one of a plurality of categories based on the relaxed feature-normalized data set, and the trained neural network is deployed.

Relaxed instance frequency normalization for neural-network-based audio processing

Embodiment systems and methods for presenting a facial expression in a virtual meeting may include detecting a user facial expression of a user based on information received from a sensor of the computing device, determining whether the detected user facial expression is approved for presentation on an avatar in a virtual meeting, generating an avatar exhibiting a facial expression consistent with the detected user facial expression in response to determining that the detected user facial expression is approved for presentation on an avatar in the virtual meeting, generating an avatar exhibiting a facial expression that is approved for presentation in response to determining that the detected user facial expression is not approved for presentation on an avatar in the virtual meeting, and presenting the generated avatar in the virtual meeting.

Presenting a facial expression in a virtual meeting

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Incheon, South Korea

Seunghan Yang