Apple Inc.

Amazon Technologies, Inc.

Srikanth Vishnubhotla

Chaitanya Mannemala

Garrett L Weinberg

Sachin S Kajarekar

Sreeneel K Maddika

Saurabh Adya

Ahmed Serag El Din Hussen Abdelaziz

Ahmed S Hussen Abdelaziz

Devang Kalidas Naik

Katrin Kirchhoff

Barry-John Theobald

Alkeshkumar M Patel

Srikanth Ronanki

Xiaochuan Niu

Sameer Badaskar

Hadis Nosrati

Jeffrey John Farris

Erik Marchi

Dhanush Bekal Kannangola

Seyedmahdad Mirsamadi

Pranay Dighe

Ognjen Rudovic

Akanksha Bindal

Ahmed H Tewfik

Alexander W Churchill

Goeric Sydney Huybrechts

Xilai Li

Techniques for customizable latency, from the customer's side, for automatic speech recognition (ASR) are described. In particular, the customer may specify a parameter that controls how fast or how slow the customer's media content will be streamed or processed. Slower processing means higher accuracy, with near real-time latency, while faster processing means lower accuracy, but offers much lower latency (e.g., less than 600 ms). Enabling tuning of the latency-versus-accuracy tradeoff of the ASR system offers customers the flexibility to meet varying needs for different ASR applications.

Customizable latency for automatic speech recognition

Systems and processes for operating an intelligent automated assistant are provided. For example, a first speech input is received from a user. In response to receiving the first speech input, a response is provided. A first output is provided corresponding to a digital assistant in a first state, and a second speech input is received from the user. A first plurality of values is obtained. Based on the first plurality of values, a first confidence level corresponding to the second speech input is obtained. In accordance with a determination that the first confidence level exceeds a first threshold confidence level, a second output is provided corresponding to the digital assistant in a second state. The second speech input continues to be received.

Multiple state digital assistant for continuous dialog

An example process includes: receiving a speech input representing a user utterance; determining, based on a textual representation of the speech input, a first score corresponding to a type of the user utterance; determining, based on the textual representation of the speech input, a second score representing a correspondence between the user utterance and a domain recognized by a digital assistant; determining, based on the first score and the second score, whether the speech input is intended for the digital assistant; in accordance with a determination that the speech input is intended for the digital assistant: initiating, by the digital assistant, a task based on the speech input; and providing an output indicative of the initiated task.

Determining whether speech input is intended for a digital assistant

Systems and processes for operating a digital assistant are provided. An example method for processing an image include receiving an image, generating, based on the image, a question corresponding to a first object in the image, generating, based on the image, a caption corresponding to a second object of the image, receiving an utterance from a user, and determining a plurality of speech recognition results from the utterance based on the question and the caption.

Using visual context to improve a virtual assistant

An example process includes: receiving an audio stream; determining a plurality of acoustic representations of the audio stream, where each acoustic representation of the plurality of acoustic representations corresponds to a respective frame of the audio stream; obtaining a respective plurality of scores indicating whether each respective frame of the audio stream is directed to an electronic device, where the obtaining includes: determining, using a triggering model operating on the electronic device, for each acoustic representation, a score indicating whether the respective frame of the audio stream is directed to the electronic device; determining, based on the respective plurality of scores, a likelihood that the audio stream is directed to the electronic device; determining whether the likelihood is above or below a threshold; and in response to determining that the likelihood is below the threshold, ceasing to process the audio stream.

Reducing device processing of unintended audio

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Santa Clara, CA, United States of America

Srikanth Vishnubhotla