Amazon Technologies, Inc.

Chungnam Chan

Chao Wang

Ming Sun

Mathias Jourdain

Cengiz Erbas

Sheetal Laad

Harshavardhan Sundar

Aaron David Wirshba

Nipul Bharani

Jialiang Bao

Rahul Gupta

Spyridon Matsoukas

Chengwei Su

Sankaranarayanan Ananthakrishnan

Shirin Saleem

Mallory McManamon

Luca Soldaini

Yugang Li

Techniques for detecting certain acoustic events from audio data are described. A system may perform event aggregation for certain types of events before sending an output to a device representing the event is detected. The system may bypass the event aggregation process for certain types of events that the system may detect with a high level of confidence. In such cases, the system may send an output to the device when the event is detected. The system may be used to detect acoustic events representing presence of a person or other harmful circumstances (such as, fire, smoke, etc.) in a home, an office, a store, or other types of indoor settings.

Acoustic event detection

Techniques for processing a user input are described. Text data representing a user input is processed with respect to at least one finite state transducer (FST) to generate at least one FST hypothesis. Context information may be required to traverse one or more paths of the at least one FST. The text data is also processed using at least one statistical model (e.g., perform intent classification, named entity recognition, and/or domain classification processing) to generate at least one statistical model hypothesis. The at least one FST hypothesis and the at least one statistical model hypothesis are input to a reranker that determines a most likely interpretation of the user input.

Natural language understanding processing

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Westford, MA, United States of America

Chungnam Chan