Amazon Technologies, Inc.

Nishant Kumar

Sumedha Arvind Kshirsagar

Ajay Gopalakrishnan

Jeff Bradley Beal

Kevin Robert Charter

Frederic Johan Georges Deramat

Shamitha Somashekar

Steven Michael Saxon

Gautham Kumar Jayakumar

Natalia Vladimirovna Mamkina

Naomi Bancroft

David Robert Thomas

Vikas Jain

Shishir Sridhar Bharathi

A speech recognition platform configured to receive an audio signal that includes speech from a user and perform automatic speech recognition (ASR) on the audio signal to identify ASR results. The platform may identify: (i) a domain of a voice command within the speech based on the ASR results and based on context information associated with the speech or the user, and (ii) an intent of the voice command. In response to identifying the intent, the platform may perform multiple actions corresponding to this intent. The platform may select a target action to perform, and may engage in a back-and-forth dialog to obtain information for completing the target action. The action may include streaming audio to the device, setting a reminder for the user, purchasing an item on behalf of the user, making a reservation for the user or launching an application for the user.

Outcome-oriented dialogs on a speech recognition platform

Systems, methods, and devices for recognizing a user are disclosed. A speech-controlled device captures a spoken utterance, and sends audio data corresponding thereto to a server. The server determines content sources storing or having access to content responsive to the spoken utterance. The server also determines multiple users associated with a profile of the speech-controlled device. Using the audio data, the server may determine user recognition data with respect to each user indicated in the speech-controlled device's profile. The server may also receive user recognition confidence threshold data from each of the content sources. The server may determine user recognition data associated that satisfies (i.e., meets or exceeds) a most stringent (i.e., highest) of the user recognition confidence threshold data. Thereafter, the server may send data indicating a user associated with the user recognition data to all of the content sources.

User recognition for speech processing systems

Systems and methods for audio output control are disclosed. Audio may be output via a speaker of a communal device associated with a first portion of an environment. A user may provide a user utterance indicating an intent to add another device in a second portion of the environment to the audio-output session, and/or an intent to move the audio-output session from the first device to the second device, and/or an intent to remove a device from an audio-output session. Based on this determined intent, audio-session queues may be associated and dissociated from devices and device states may be altered to effectuate the intent of the user utterance.

Audio output control

Features are disclosed for performing functions in response to user requests. Natural Language Understanding ('NLU') processing may be performed to generate command data that represents a subject of an utterance. The command data may be sent to an application that causes presentation of first output content in a first modality at a first time in response to receiving the command data, and generates second output content in a second modality different from the first modality, wherein the second output content is associated with the first output content. The second output content may be presented in the second modality at a second time subsequent to the first time.

Dialog management with multiple modalities

Features are disclosed for performing functions in response to user requests based on contextual data regarding prior user requests. Users may engage in conversations with a computing device in order to initiate some function or obtain some information. A dialog manager may manage the conversations and store contextual data regarding one or more of the conversations. Processing and responding to subsequent conversations may benefit from the previously stored contextual data by, e.g., reducing the amount of information that a user must provide if the user has already provided the information in the context of a prior conversation. Additional information associated with performing functions responsive to user requests may be shared among applications, further improving efficiency and enhancing the user experience.

Dialog management with multiple applications

Dialog management context sharing

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Sunnyvale, CA, United States of America

Nishant Kumar