Amazon Technologies, Inc.

Zeya Chen

William Evan Welbourne

Sven Eberhardt

Charles Edwin Ashton Brett

Sara Parker Hillenmeyer

Aniruddha Basak

Lizhen Peng

Jay Patel

Yunfeng Jiang

Akshay Kumar

Hongyang Wang

James G Droppo

Oguz Hasan Elibol

Roberto Barra Chicote

Jie Liang

Yixiong Meng

Grzegorz Beringer

Chia-Hao Chang

Systems and methods for intelligent device grouping are disclosed. An environment, such as a home, may have a number of voice-enabled devices and accessory devices that may be controlled by the voice-enabled devices. One or more models, such as linguistics model(s) and/or device affinity models may be utilized to determine which accessory devices are candidates for inclusion in a device group, and a recommendation for grouping the devices may be provided. Device-group naming recommendations may also be generated and may be sent to users.

Intelligent device grouping

Techniques for generating, from first speaker recognition data corresponding to at least a first word, second speaker recognition data corresponding to at least a second word are described. During a speaker recognition enrollment process, a device receives audio data corresponding to one or more prompted spoken inputs comprising the at least first word. Using the prompted spoken input(s), the first speaker recognition data (specific to that least first word) is generated. Sometime thereafter, a user may indicate that speaker recognition processing is to be performed using at least a second word. Rather than have the user go through the speaker recognition enrollment process a second time, the device (or a system) may apply a transformation model to the first speaker recognition data to generate second speaker recognition data specific to the at least second word.

Speaker recognition adaptation

A speech-processing system receives input data representing text. A first encoder processes segments of the text to determine embedding data representing the text, and a second encoder processes corresponding audio data to determine prosodic data corresponding to the text. The embedding and prosodic data is processed to create output data including a representation of speech corresponding to the text and prosody.

Synthetic speech processing

Described are systems, methods, and apparatus that gathers environment condition data from different sensors at various locations within an area, aggregates the environment condition data to produce aggregated environment condition scores for the area and provides the aggregated environment condition scores to different locations within the area. While sensor data from a single sensor/device, such as a camera may provide low quality environment information, by collecting and aggregating information from multiple sensors and/or locations in the area, highly accurate aggregated environment condition scores for environment conditions may be realized. The aggregated environment condition scores may be provided to various locations within the area as representative of the environment condition at that point in time within the area, regardless of whether those locations have sensors. The aggregated environment condition scores may be used by other devices at those locations to automate one or more actions, such as adjusting lighting conditions, closing garage doors, adjusting window blind positions, etc.

Aggregating sensor data for environment conditions

Growing community of inventors

Charlottesville, VA, United States of America

Zeya Chen