Apple Inc.

Matthew S Seigel

Matthias Paulik

Ashish Garg

Rushin N Shah

Harry Joseph Saddler

Robert A Walker

Shweta Grampurohit

Sachin S Kajarekar

Henry Graham Mason

Lin Li

Gennaro Frazzingaro

Blaise Thomson

Udhyakumar Nallasamy

Anuj Kumar

Systems and processes for operating an intelligent automated assistant are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, obtaining a first set of data corresponding to one or more interactions between a user and the digital assistant on the electronic device; obtaining a second set of data corresponding to one or more interactions between the user and an application on the electronic device; and storing the first set of data and the second set of data. The method further includes receiving a set of executable instructions; processing, based on the received set of executable instructions, the first set of data and the second set of data to obtain a third set of data; and transmitting the third set of data to a remote electronic device.

Feedback analysis of a digital assistant

Speech recognition is performed on a received utterance to determine a plurality of candidate text representations of the utterance, including a primary text representation and one or more alternative text representations. Natural language processing is performed on the primary text representation to determine a plurality of candidate actionable intents, including a primary actionable intent and one or more alternative actionable intents. A result is determined based on the primary actionable intent. The result is provided to the user. A recognition correction trigger is detected. In response to detecting the recognition correction trigger, a set of alternative intent affordances and a set of alternative text affordances are concurrently displayed.

User interface for correcting recognition errors

Systems and processes for automatic accent detection are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a user input, determining a first similarity between a representation of the user input and a first acoustic model of a plurality of acoustic models, and determining a second similarity between the representation of the user input and a second acoustic model of the plurality of acoustic models. The method further includes determining whether the first similarity is greater than the second similarity. In accordance with a determination that the first similarity is greater than the second similarity, the first acoustic model may be selected; and in accordance with a determination that the first similarity is not greater than the second similarity, the second acoustic model may be selected.

Automatic accent detection using acoustic models

Systems and processes for evaluating embedded personalized systems are provided. In one example process, instructions that define an experiment associated with a personalized speech recognition system can be received. The instructions can define one or more experimental parameters. In accordance with the received instructions, a second personalized speech recognition system can be generated based on the personalized speech recognition system and the one or more experimental parameters. Additionally, the plurality of user speech samples can be processed using the second personalized speech recognition system to generate a plurality of speech recognition results and a plurality of accuracy scores corresponding to the plurality of speech recognition results. Second instructions can be received based on the plurality of accuracy scores. In accordance with the second instructions, the second speech recognition system can be activated.

Privacy preserving distributed evaluation framework for embedded personalized systems

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Cambridge, United Kingdom

Matthew S Seigel