Amazon Technologies, Inc.

Steven Mack Saunders

Joe Pemberton

Isaac Joseph Madwed

Nikko Strom

Chenlei Guo

Michael F Schwartz

Young-Bum Kim

David Robert Thomas

Alexander Say Go

Rajesh Kumar Pandey

Julia Kennedy Nemer

Shuting Tang

Arun Kumar Thenappan

Archit Jain

Sungjin Lee

Zhenyu Yao

Anna Caitlin Jentoft

Pablo Cesar Ganga

Siddharth Mohan Misra

Joo-Kyung Kim

Laura Maggia Panfili

Techniques for performing runtime ranking of skill components are described. A skill developer may generate a rule indicating a skill component is to be invoked at runtime when a natural language input corresponds to a specific context. At runtime, a virtual assistant system may implement a machine learned model to generate an initial ranking of skill components. Thereafter, the virtual assistant system may use skill component-specific rules to adjust the initial ranking, and this second ranking is used to determine which skill component to invoke to respond to the natural language input. Overtime, if a rule results in beneficial user experiences, the virtual assistant system may incorporate the rule into the machine learned model.

Natural language input routing

Described herein is a system for responding to a frustrated user with a response determined based on spoken language understanding (SLU) processing of a user input. The system detects user frustration and responds to a repeated user input by confirming an action to be performed or presenting an alternative action, instead of performing the action responsive to the user input. The system also detects poor audio quality of the captured user input, and responds by requesting the user to repeat the user input. The system processes sentiment data and signal quality data to respond to user inputs.

Sentiment aware voice user interface

Devices and techniques are generally described for using user feedback to determine routing decisions in a speech processing system. In various examples, first data representing a first utterance may be received. Second data representing a first semantic interpretation of the first utterance may be determined. A first intent data processing application may be selected for processing the second data. Feedback data may be determined related to the first intent data processing application processing the second data. Third data representing a semantic interpretation of a second utterance may be received, wherein the first semantic interpretation is the same as the second semantic interpretation. A second intent data processing application may be determined for processing the third data based at least in part on the feedback data.

Natural language processing

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Bellevue, WA, United States of America

Steven Mack Saunders