Qualcomm Incorporated

Electronics and Telecommunications Research Institute

Kyu Woong Hwang

Sungrack Yun

Taesu Kim

Minho Jin

Byeonggeun Kim

Hye Jin Jang

Yongwoo Cho

Jun-Cheol Cho

Min-Kyu Park

Kisun You

Duck-hoon Kim

Young Mo Kang

Mingu Lee

Janghoon Cho

Jaewon Choi

Jae-won Choi

Kang Min Kim

Simyung Chang

Juntae Lee

Hyunsin Park

Jinkyu Lee

Wonil Chang

Peter J Shah

Te-Won Lee

Kwokleung Chan

Sunkuk Moon

Jun Soo Park

Hyung-Il Koo

Hyun-Mook Cho

Seungwoo Yoo

Yin Huang

Haijun Zhao

Jinseok Lee

Sungwoong Kim

Seung Hi Kim

Hyung Bae Jeon

Minsub Lee

Ruowei Wang

Seunghan Yang

Kee-Hyun Park

Chunghoon Kim

Hanul Kim

Dooyong Sung

Seong Ill Park

Jangho Kim

Yeonseok Kim

Hyoungwoo Park

A method for generating a personalized model includes receiving one or more personal data samples from a user. A prototype of a personal identity is generated based on the personal data samples. The prototype of the personal identity is trained to reflect personal characteristics of the user. A network graph is generated based on the prototype of the personal identity. One or more channels of a global network are pruned based on the network graph to produce the personalized model.

Personalized neural network pruning

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 are generally directed to apparatus and techniques for event state detection. One example method generally includes receiving a plurality of sensor signals at a computing device, determining, at the computing device, probabilities of sub-event states based on the plurality of sensor signals using an artificial neural network for each of a plurality of time intervals, and detecting, at the computing device, the event state based on the probabilities of the sub-event states via a state sequence model.

Vehicle entry detection

System and method for operating an always-on ASR (automatic speech recognition) system by selecting target keywords and continuously detecting the selected target keywords in voice commands in a mobile device are provided. In the mobile device, a processor is configured to collect keyword candidates, collect usage frequency data for keywords in the keyword candidates, collect situational usage frequency data for the keywords in the keyword candidates, select target keywords from the keyword candidates based on the usage frequency data and the situational usage frequency data, and detect one or more of the target keywords in a voice command using continuous detection of the target keywords.

Target keyword selection

A method for operating a neural network includes receiving an input sequence at an encoder. The input sequence is encoded to produce a set of hidden representations. Attention-heads of the neural network calculate attention weights based on the hidden representations. A context vector is calculated for each attention-head based on the attention weights and the hidden representations. Each of the context vectors correspond to a portion of the input sequence. An inference is output based on the context vectors.

Orthogonally constrained multi-head attention for speech tasks

Imaging systems and techniques are described. An imaging system receives image data representing at least a portion (e.g., a face) of a first user as captured by a first image sensor. The imaging system identifies that a gaze of the first user as represented in the image data is directed toward a displayed representation of at least a portion (e.g., a face) of a second user. The imaging system identifies an arrangement of representations of users for output. The imaging system generates modified image data based on the gaze and the arrangement at least in part by modifying the image data to modify at least the portion of the first user in the image data to be visually directed toward a direction corresponding to the second user based on the gaze and the arrangement. The imaging system outputs the modified image data arranged according to the arrangement.

Systems and methods of image processing based on gaze detection

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

A method of determining, by an electronic device, an audio event is disclosed. The method may include receiving an input sound from a sound source by a plurality of sound sensors. The method may also extracting, by a processor, at least one sound feature from the received input sound, determining, by the processor, location information of the sound source based on the input sound received by the sound sensors, determining, by the processor, the audio event indicative of the input sound based on the at least one sound feature and the location information, and transmitting, by a communication unit, a notification of the audio event to an external electronic device.

Determining audio event based on location information

A processor is configured to transition in and out of a low-power state at a first rate and to operate in a first mode or a second mode. In a particular method, the processor while coupled to a coder/decoder (CODEC) retrieves audio feature data from a buffer after transitioning out of the low-power state. The CODEC is configured to operate at a second rate in the first mode and at a third rate in the second mode, the second rate and the third rate each greater than the first rate. The audio feature data indicates features of audio data received during the low-power state of the processor. A ratio of CODEC activity to processor activity in the second mode is less than the ratio in the first mode.

Extraction and analysis of buffered audio data using multiple codec rates each greater than a low-power processor rate

A method, performed by an electronic device, for verifying a user to allow access to the electronic device is disclosed. In this method, sensor data may be received from a plurality of sensors including at least an image sensor and a sound sensor. Context information of the electronic device may be determined based on the sensor data and at least one verification unit may be selected from a plurality of verification units based on the context information. Based on the sensor data from at least one of the image sensor or the sound sensor, the at least one selected verification unit may calculate at least one verification value. The method may determine whether to allow the user to access the electronic device based on the at least one verification value and the context information.

Context-based access verification

Growing community of inventors

Taejon, South Korea

Kyu Woong Hwang