Amazon Technologies, Inc.

Cengiz Erbas

Chao Wang

Spyridon Matsoukas

Ming Sun

Avnish Sikka

Mathias Jourdain

Simon Peter Reavely

Thomas Kollar

Chungnam Chan

Aaron David Wirshba

Nipul Bharani

Jialiang Bao

Sheetal Laad

Harshavardhan Sundar

A system capable of performing natural language understanding (NLU) on utterances including complex command structures such as sequential commands (e.g., multiple commands in a single utterance), conditional commands (e.g., commands that are only executed if a condition is satisfied), and/or repetitive commands (e.g., commands that are executed until a condition is satisfied). Audio data may be processed using automatic speech recognition (ASR) techniques to obtain text. The text may then be processed using machine learning models that are trained to parse text of incoming utterances. The models may identify complex utterance structures and may identify what command portions of an utterance go with what conditional statements. Machine learning models may also identify what data is needed to determine when the conditionals are true so the system may cause the commands to be executed (and stopped) at the appropriate times.

Processing complex utterances for natural language understanding

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

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Acton, MA, United States of America

Cengiz Erbas