Qualcomm Incorporated

Sungrack Yun

Kyu Woong Hwang

Taesu Kim

Hye Jin Jang

Min-Kyu Park

Byeonggeun Kim

Young Mo Kang

Jun-Cheol Cho

Fatih Murat Porikli

Kang Min Kim

Minho Jin

Kee-Hyun Park

Janghoon Cho

Heeman Kim

Hanul Kim

Jae-won Choi

Mustafa Keskin

Duck Hoon Kim

Seungwoo Yoo

Sungwoong Kim

Yongwoo Cho

Mihir Jain

Kyuwoong Hwang

Minsub Lee

Juntae Lee

Seunghan Yang

Debasmit Das

Hyoungwoo Park

Aspects presented herein may enable a wireless device to dynamically change the frequencies of its pilot tones based on the distance of one or more objects detected, thereby enabling the wireless device to utilize the advantages of both high frequency pilot tone and low frequency pilot tone. In one aspect, a wireless device transmits a first pilot tone at a first frequency. The wireless device detects whether there is an object within a specified distance of the wireless device based on a reflected signal of the first pilot tone. The wireless device transmits a second pilot tone at a second frequency based on at least one object being detected within the specified distance, where the second frequency is higher than the first frequency. The wireless device calculates a first distance of the at least one object with respect to the wireless device based the second pilot tone.

Acoustic-based positioning with dynamic frequency pilot tone

A processor-implemented method for recognizing a natural language on a mobile device includes receiving an audio input. The method further includes using a neural network to generate local text corresponding to the audio input. The method still further includes generating a local confidence value for accuracy of the local text. The method includes transmitting, to a remote device, data corresponding to the audio input. The method further includes receiving remote text corresponding to the data, along with a remote confidence score for accuracy of the remote text. The method still further includes outputting the local text in response to the local confidence value being higher than the remote confidence score, and outputting the remote text in response to the remote confidence score being higher than the local confidence value.

Hybrid language translation on mobile devices

Certain aspects of the present disclosure provide a method of processing video data. In one example, the method includes receiving input video data; sampling a first subset of clips from the input video data; providing the first subset of clips to a first component of a machine learning model to generate first output; sampling a second subset of clips from the input video data, wherein the second subset of clips comprises fewer clips than the first subset of clips; providing the second subset of clips to a second component of the machine learning model to generate a second output; aggregating the first output from the first component of the machine learning model with the second output from the second component of the machine learning model to generate aggregated output; and determining a characteristic of the input video data based on the aggregated output.

Efficient video processing via dynamic knowledge propagation

Certain aspects of the present disclosure provide techniques for improved domain adaptation in machine learning. A feature tensor is generated by processing input data using a feature extractor. A first set of logits is generated by processing the feature tensor using a domain-agnostic classifier, and a second set of logits is generated by processing the feature tensor using a domain-specific classifier. A loss is computed based at least in part on the first set of logits and the second set of logits, where the loss includes a divergence loss component. The feature extractor, the domain-agnostic classifier, and the domain-specific classifier are refined using the loss.

Model disentanglement for domain adaptation

In one embodiment, an electronic device includes an input device configured to provide an input stream, a first processing device, and a second processing device. The first processing device is configured to use a keyword-detection model to determine if the input stream comprises a keyword, wake up the second processing device in response to determining that a segment of the input stream comprises the keyword, and modify the keyword-detection model in response to a training input received from the second processing device. The second processing device is configured to use a first neural network to determine whether the segment of the input stream comprises the keyword and provide the training input to the first processing device in response to determining that the segment of the input stream does not comprise the keyword.

On-device self training in a two-stage wakeup system comprising a system on chip which operates in a reduced-activity mode

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

A device to process an audio signal representing input sound includes a hand detector configured to generate a first indication responsive to detection of at least a portion of a hand over at least a portion of the device. The device also includes an automatic speech recognition system configured to be activated, responsive to the first indication, to process the audio signal.

Activating speech recognition based on hand patterns detected using plurality of filters

A device to process an audio signal representing input sound includes a user voice verifier configured to generate a first indication based on whether the audio signal represents a user's voice. The device includes a speaking target detector configured to generate a second indication based on whether the audio signal represents at least one of a command or a question. The device includes an activation signal unit configured to selectively generate an activation signal based on the first indication and the second indication. The device also includes an automatic speech recognition engine configured to be activated, responsive to the activation signal, to process the audio signal.

Method and apparatus for activating speech recognition

A device to perform end-of-utterance detection includes a speaker vector extractor configured to receive a frame of an audio signal and to generate a speaker vector that corresponds to the frame. The device also includes an end-of-utterance detector configured to process the speaker vector and to generate an indicator that indicates whether the frame corresponds to an end of an utterance of a particular speaker.

Method and apparatus for detecting an end of an utterance

A device includes a screen and one or more processors configured to provide, at the screen, a graphical user interface (GUI) configured to display data associated with multiple devices on the screen. The GUI is also configured to illustrate a label and at least one control input for each device of the multiple devices. The GUI is also configured to provide feedback to a user. The feedback indicates that a verbal command is not recognized with an action to be performed. The GUI is also configured to provide instructions for the user on how to teach the one or more processors which action is to be performed in response to receiving the verbal command.

Virtual assistant device

An electronic device for generating video data is disclosed. The electronic device may include a communication unit configured to wirelessly receive a video stream captured by a camera, wherein the camera is located in an unmanned aerial vehicle. The electronic device may also include at least one sound sensor configured to receive an input sound stream. In addition, the electronic device may include an audio control unit configured to generate an audio stream associated with the video stream based on the input sound stream. Further, the electronic device may include a synthesizer unit configured to generate the video data based on the video stream and the audio stream.

Electronic device for generating video data

A method, which is performed by an electronic device, for obtaining a speaker-independent keyword model of a keyword designated by a user is disclosed. The method may include receiving at least one sample sound from the user indicative of the keyword. The method may also generate a speaker-dependent keyword model for the keyword based on the at least one sample sound, send a request for the speaker-independent keyword model of the keyword to a server in response to generating the speaker-dependent keyword model, and receive the speaker-independent keyword model adapted for detecting the keyword spoken by a plurality of users from the server.

Keyword detection using speaker-independent keyword models for user-designated keywords

According to an aspect of the present disclosure, a method for generating a keyword model of a user-defined keyword in an electronic device is disclosed. The method includes receiving at least one input indicative of the user-defined keyword, determining a sequence of subwords from the at least one input, generating the keyword model associated with the user-defined keyword based on the sequence of subwords and a subword model of the subwords, wherein the subword model is configured to model a plurality of acoustic features of the subwords based on a speech database, and providing the keyword model associated with the user-defined keyword to a voice activation unit configured with a keyword model associated with a predetermined keyword.

Keyword model generation for detecting user-defined keyword

A method for generating a notification by an electronic device to alert a user of the electronic device is disclosed. In this method, a speech phrase may be received. Then, the received speech phrase may be recognized, by a processor, as a command to generate the notification. In addition, one or more context data of the electronic device may be detected by at least one sensor. It may be determined whether the notification is to be generated at least based on the context data. The notification may be generated, by the processor, based on the context data and the command to generate the notification.

Electronic device generating notification based on context data in response to speech phrase from user

A method for controlling an electronic device in response to speech spoken by a user is disclosed. The method may include receiving an input sound by a sound sensor. The method may also detect the speech spoken by the user in the input sound, determine first characteristics of a first frequency range and second characteristics of a second frequency range of the speech in response to detecting the speech in the input sound, and determine whether a direction of departure of the speech spoken by the user is toward the electronic device based on the first and second characteristics.

Growing community of inventors

Seongnam, South Korea

Sungrack Yun