An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

The various methods and devices described herein relate to devices which, in at least certain embodiments, may include one or more sensors for providing data relating to user activity and at least one processor for causing the device to respond based on the user activity which was determined, at least in part, through the sensors. The response by the device may include a change of state of the device, and the response may be automatically performed after the user activity is determined.

Automated Response to and Sensing of User Activity in Portable Devices

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

A method and apparatus are provided for building an intelligent automated assistant. Embodiments of the present invention rely on the concept of “active ontologies” (e.g., execution environments constructed in an ontology-like manner) to build and run applications for use by intelligent automated assistants. In one specific embodiment, a method for building an automated assistant includes interfacing a service-oriented architecture that includes a plurality of remote services to an active ontology, where the active ontology includes at least one active processing element that models a domain. At least one of the remote services is then registered for use in the domain.

Method and apparatus for building an intelligent automated assistant

Among other things, techniques and systems are disclosed for implementing contextual voice commands. On a device, a data item in a first context is displayed. On the device, a physical input selecting the displayed data item in the first context is received. On the device, a voice input that relates the selected data item to an operation in a second context is received. The operation is performed on the selected data item in the second context.

Contextual voice commands

In this paper, “continuous speech recognition” problem is given a clear mathematical formulation as the search for that sequence of basic speech units that best fits the input acoustic pattern. For this purpose spoken language models in the form of hierarchical transition networks are introduced, where lower level subnetworks describe the basic units as possible sequences of spectral states. The units adopted in this paper are either whole words or smaller subword elements, called diphones. The recognition problem thus becomes that of finding the best path through the network, a task carried out by the linguistic decoder. By using this approach, knowledge sources at different levels are strongly integrated. In this way, early decision making based on partial information (in particular any segmentation operation or the speech/silence distinction) is avoided; usually this is a significant source or errors. Instead, decisions are deferred to the linguistic decoder, which possesses all the necessary pieces of information. The properties that a linguistic decoder must possess in order to operate in real-time are listed, and then a best-few algorithm with partial traceback of explored paths, satisfying the above requisites, is described. In particular, the amount of storage needed is almost constant for any sentence length, the computation is approximately linear with sentence length, and the interpretation of early words in a sentence may be possible long before the speaker has finished talking. Experimental results with two systems, one with words and the other with diphones as basic speech units, are reported. Finally, relative merits of words and diphones are discussed, taking into account aspects such as the storage and computing time requirements, their relative ability to deal with phonological variations and to discriminate between similar words, their speaker adaptation capability, and the ease with which it is possible to change the vocabulary and the language dependencies.

Carlo Scagliola

Papers

Language models and search algorithms for real-time speech recognition