Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Personalized recommendations based on related users

Abstract: This disclosure describes systems and associated processes for generating recommendations for users based on usage, among other things. These systems and processes are described in the context of an interactive computing system that enables users to download applications for mobile devices (such as phones) or for other computing devices. Users' interactions with applications once they are downloaded can be observed and tracked, with such usage data being collected and provided to the interactive computing system. The interactive computing system can include a recommendation system or service that processes the usage data from a plurality of users to detect usage patterns. Using these usage patterns, among possibly other data, such as data about related users' applications, the recommendation system can recommend applications to users for download.

Monophone-Based Background Modeling for Two-Stage On-Device Wake Word Detection

Abstract: Accurate on-device wake word detection is crucial to products with far-field voice control such as the Amazon Echo. It is quite challenging to build a wake word system with both low False Reject Rate (FRR) and low False Alarm Rate (FAR) in real scenarios where there are various types of background speech, music or noise, especially when computational resources on the device is limited. In this paper, we introduce a two-stage wake word system based on Deep Neural Network (DNN) acoustic modeling, propose a new way to model the non-keyword background events using monophone-based units and present how richer information can be extracted from those monophone units for final wake word detection. Under the new system, we could get around 16% relative reduction in FRR when fixing the false alarm level, and about 37% relative reduction in FAR on the other hand if we maintain the miss rate. For the 2nd stage classifier itself, it is able to reduce the false alarm rate relatively by about 67% on top of 1st stage hypothesis with very few computational resources.

Software container registry service

Abstract: A request to store, in first data store associated with a customer of a computing resource service provider, a software image is received, the request including a set of layers of the software image to be stored. As a result of successful authentication of the request, based at least in part on a security token included with the request, a subset of layers of the software image that have not previously been stored in the first data store are determined, based at least in part on first metadata obtained from a second data store, the subset of layers in the first data store are stored, second metadata about the subset of layers are stored in the second data store, and the software image is caused to be launched in a software container of an instance based at least in part on the subset of layers.

Method and system for access to electronic images of text based on user ownership of corresponding physical text

Abstract: Methods and apparatus of the invention enable users to request access to one or more electronic images of pages in a physical text. When the user is identified and user ownership of the physical text is confirmed, the user is given access to the requested electronic images in accordance with the one or more access rules. Electronic images of pages may be automatically added to a user-personalized library of electronic content for later access. A flag associated with the user and the pages images may be set to indicate confirmed user ownership of the physical text. A user may purchase a physical text itself or purchase an item that the physical text normally accompanies. Electronic page images may be acquired by scanning printed pages of the text or from a user upload. Access to the electronic images of a physical text is based on user ownership of the physical text.

Identification of utterance subjects

Abstract: Features are disclosed for generating markers for elements or other portions of an audio presentation so that a speech processing system may determine which portion of the audio presentation a user utterance refers to. For example, an utterance may include a pronoun with no explicit antecedent. The marker may be used to associate the utterance with the corresponding content portion for processing. The markers can be provided to a client device with a text-to-speech ("TTS") presentation. The markers may then be provided to a speech processing system along with a user utterance captured by the client device. The speech processing system, which may include automatic speech recognition ("ASR") modules and/or natural language understanding ("NLU") modules, can generate hints based on the marker. The hints can be provided to the ASR and/or NLU modules in order to aid in processing the meaning or intent of a user utterance.