Apple Inc.

Devang Kalidas Naik

Kim Silverman

Jerome Rene Bellegarda

Kim E A Silverman

Christopher Brian Fleizach

Eric Taylor Seymour

Ryan S Dixon

Hong M Chen

Benjamin A Rottler

Philippe P Piernot

Gregory F Hughes

Justin G Binder

Ian M Fisch

Darren Christopher Minifie

Joel M Lopes Da Silva

Michael M Pedersen, Ii

Kevin Lenzo

Ryan N Dour

Matthew Lee Rogers

Thomas R Gruber

Lia T Napolitano

Sachin S Kajarekar

Gunnar Evermann

Jannes G A Dolfing

Rushin N Shah

Kevin Bartlett Aitken

Caroline G Henton

Shaun Eric Williams

Charles Srisuwananukorn

Matthias Neeracher

Liam Weiner

Kim Ernest Alexander Silverman

Kevin Tiene

Steve S Ko

Baptiste Pierre Paquier

Guy Leslie Tribble

Onur E Tackin

Mallory Paine

Sreeneel K Maddika

Douglas R Davidson

Brent D Ramerth

John William Scalo

Sarmad Aziz Malik

Jennifer Lauren Moore

Srikanth Vishnubhotla

Jia Pu

Joshua Matthew Williams

Erik Marchi

Ali S Mohamed

ShawShin Zhang

Kim Ea Silverman

Pranay Dighe

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

Systems and processes are disclosed for virtual assistant request recognition using live usage data and data relating to future events. User requests that are received but not recognized can be used to generate candidate request templates. A count can be associated with each candidate request template and can be incremented each time a matching candidate request template is received. When a count reaches a threshold level, the corresponding candidate request template can be used to train a virtual assistant to recognize and respond to similar user requests in the future. In addition, data relating to future events can be mined to extract relevant information that can be used to populate both recognized user request templates and candidate user request templates. Populated user request templates (e.g., whole expected utterances) can then be used to recognize user requests and disambiguate user intent as future events become relevant.

Method for extracting salient dialog usage from live data

Systems and processes for operating an intelligent automated assistant are provided. An examples process of operating an intelligent automated assistant includes, at an electronic device with one or more processors and memory, receiving audio input, determining a direct-to-reverberant energy ratio based on the audio input, and determining a head pose of a user based on the direct-to-reverberant energy ratio.

Determining head pose based on room reverberation

Systems and methods are provided for associating a phonetic pronunciation with a name by receiving the name, mapping the name to a plurality of monosyllabic components that are combinable to construct the phonetic pronunciation of the name, receiving a user input to select one or more of the plurality, and combining the selected one or more of the plurality of monosyllabic components to construct the phonetic pronunciation of the name.

Systems and methods for name pronunciation

Techniques for providing reminders based on social interactions between users of electronic devices are described. Social reminders can be set to trigger based on social interactions of users. For example, a user may request to be reminded to discuss a certain discussion topic with a particular phonebook contact, when the user next encounters the contact.

Social reminders

In some implementations, a user device can personalize a media stream by converting notifications into audio speech data and presenting the audio speech data at locations within the media stream that do not interrupt the enjoyment of the media stream by the user. In some implementations, the user device can receive notifications from various communication services, applications installed on the user device, and/or other sources, determine information describing the notifications, and present the information to the user using the audio speech data. In some implementations, the user device can generate personalized notifications based on the media stream and/or media items selected by the user. The user device can generate personalized notifications based on the user's context (e.g., environment, location, activity, etc.). The personalized notifications can then be presented to the user using audio speech data at appropriate locations in the media stream.

Personalization of media streams

Systems and processes for robust end-pointing of speech signals using speaker recognition are provided. In one example process, a stream of audio having a spoken user request can be received. A first likelihood that the stream of audio includes user speech can be determined. A second likelihood that the stream of audio includes user speech spoken by an authorized user can be determined. A start-point or an end-point of the spoken user request can be determined based at least in part on the first likelihood and the second likelihood.

Robust end-pointing of speech signals using speaker recognition

A speech recognition system uses, in one embodiment, an extended phonetic dictionary that is obtained by processing words in a user's set of databases, such as a user's contacts database, with a set of pronunciation guessers. The speech recognition system can use a conventional phonetic dictionary and the extended phonetic dictionary to recognize speech inputs that are user requests to use the contacts database, for example, to make a phone call, etc. The extended phonetic dictionary can be updated in response to changes in the contacts database, and the set of pronunciation guessers can include pronunciation guessers for a plurality of locales, each locale having its own pronunciation guesser.

Name recognition system

While an electronic device with a display and a touch-sensitive surface is in a screen reader accessibility mode, the device displays a character input area and a keyboard, the keyboard including a plurality of key icons. The device detects a sequence of one or more gestures on the touch-sensitive surface that correspond to one or more characters. A respective gesture of the one or more gestures that corresponds to a respective character is a single finger gesture that moves across the touch-sensitive surface along a respective path that corresponds to the respective character. The respective path traverses one or more locations on the touch-sensitive surface that correspond to one or more key icons of the plurality of key icons without activating the one or more key icons. In response to detecting the respective gesture, the device enters the corresponding respective character in the character input area of the display.

Device, method, and graphical user interface for integrating recognition of handwriting gestures with a screen reader

The method is performed at an electronic device with one or more processors and memory storing one or more programs for execution by the one or more processors. A first speech input including at least one word is received. A first phonetic representation of the at least one word is determined, the first phonetic representation comprising a first set of phonemes selected from a speech recognition phonetic alphabet. The first set of phonemes is mapped to a second set of phonemes to generate a second phonetic representation, where the second set of phonemes is selected from a speech synthesis phonetic alphabet. The second phonetic representation is stored in association with a text string corresponding to the at least one word.

System and method for user-specified pronunciation of words for speech synthesis and recognition

Systems and processes for disambiguating heteronyms in speech synthesis are provided. In one example process, a speech input containing a heteronym can be received from a user. The speech input can be processed using an automatic speech recognition system to determine a phonemic string corresponding to the heteronym as pronounced by the user in the speech input. A correct pronunciation of the heteronym can be determined based on at least one of the phonemic string or using an n-gram language model of the automatic speech recognition system. A dialogue response to the speech input can be generated where the dialogue response can include the heteronym. The dialogue response can be outputted as a speech output. The heteronym in the dialogue response can be pronounced in the speech output according to the correct pronunciation.

Disambiguating heteronyms in speech synthesis

Growing community of inventors

San Jose, CA, United States of America

Devang Kalidas Naik