Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Yaser Khan

Vikram Kumar Gundeti

Michael F Schwartz

Rongzhou Shen

Bo Li

Leopold Bushkin

Munir Mahmood

Alexander Thomas Loeb

Shemyla Anwar

Mohammed Arif

Edward Page Foyle

Alice Jane Bernheim Brush

Aleksandar Uzelac

Sergio Paolantonio

Vishwac Sena Kannan

Daniel J Hwang

Ashish Rangole

Thomas Charles Butcher

Jenny Kam

Srinivas Palla

Jaeyoun Kim

Dennis Mooney, Ii

Mark Lawrence

Chris Huybreqts

Piyush Kandpal

Jason Wang

Ritesh Patel

Dennis James Ii Mooney

Techniques for a natural language processing (NLP) system to implement more than one assistant are described. The NLP system may receive a natural language input corresponding to more than one user command. The NLP system may respond to a first command, of the natural language input, using a TTS voice of a first NLP system assistant. The NLP system may respond to a second command, of the natural language input, using a TTS voice of a second NLP system assistant.

Multi-assistant natural language input processing

A multi-assistant speech-processing system that centrally determines multiple execution plans to respond to a user input. A central component determines whether a particular input should be processed using a requested assistant or a different assistant or should be terminated. Assistant handoff may be determined based on system policies as well as user input-specific data. A ranked list of execution options may be supplemented by augmented data corresponding to messages to a user. The system may attempt to execute plans in the ranked order until a plan succeeds.

Access to multiple virtual assistants

Techniques for a natural language processing (NLP) system to implement more than one assistant are described. The NLP system may receive a natural language input from a device. The NLP system may also receive one or more signals representing one or more assistants to be implemented with respect to the natural language input. The NLP system may intelligently select an assistant to be invoked with respect to the natural language input. Once the assistant is selected, the NLP system may cause content, output to a user, to have characteristics specific to the assistant.

Multi-assistant natural language input processing to determine a voice model for synthesized speech

Systems, methods, and computer-readable storage media are provided for recognizing emotion in audio signals in real-time. An audio signal is detected and a rapid audio fingerprint is computed on a user's computing device. One or more features is extracted from the audio fingerprint and compared with features associated with defined emotions to determine relative degrees of similarity. Confidence scores are computed for the defined emotions based on the relative degrees of similarity and it is determined whether a confidence score for one or more particular emotions exceeds a threshold confidence score. If it is determined that a threshold confidence score for one or more particular emotions is exceeded, the particular emotion or emotions are associated with the audio signal. As desired, various action then may be initiated based upon the emotion/emotions associated with the audio signal.

Real-time emotion recognition from audio signals

An electronic device in a topology of interconnected electronic devices can listen for a wake phrase and voice commands. The device can control when and how it responds so that a single device responds to voice commands. Per-task device preferences can be stored for a user. If a preferred device is not available, the task can still be performed on a device that has appropriate capabilities. Machine learning can determine a user's preferences. Power conservation and effective user interaction can result.

Device arbitration for listening devices

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Kirkland, WA, United States of America

Yaser Khan