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

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5.

https://faculty.washington.edu/fxia/courses/LING580MT/yamada_2001.pdf

A Syntax-based Statistical Translation Model

https://hal.archives-ouvertes.fr/hal-00499203/document

Joint-sequence models for grapheme-to-phoneme conversion

According to some aspects, a method of monitoring an acoustic environment of a mobile device, at least one computer readable medium encoded with instructions that, when executed, perform such a method and/or a mobile device configured to perform such a method is provided. The method comprises receiving, by the mobile device, acoustic input from the environment of the mobile device, detecting whether the acoustic input includes a voice command from a user without requiring receipt of an explicit trigger from the user, and initiating responding to the detected voice command.

Methods and apparatus for detecting a voice command

Power consumption for a computing device may be managed by one or more keywords. For example, if an audio input obtained by the computing device includes a keyword, a network interface module and/or an application processing module of the computing device may be activated. The audio input may then be transmitted via the network interface module to a remote computing device, such as a speech recognition server. Alternately, the computing device may be provided with a speech recognition engine configured to process the audio input for on-device speech recognition.

Speech recognition power management

Natural language controlled devices may be configured to activate command recognition in response to one or more wake words Techniques are provided to receive a candidate word for evaluation as a wake word that activates a natural language control functionality of a computing device The candidate word may include one or more words or sounds Values for multiple wake word metrics are then determined The candidate word is evaluated based on the various wake word metrics

Wake word evaluation

A device is configured with multiple applications that each respond to various commands. The correct application to receive a natural language command is identified by consideration of how well the command matches functions of the application. A target application to receive the command may additionally be selected by consideration of which application is most likely to receive a command. The likelihood of an application to receive a command may be determined by considering context. The command may be a voice input that is analyzed by speech recognition technology to determine word strings representing possible commands. Thus, the selection of a target application to receive the command may be based on any or all of the word strings from the natural language input, a closeness of fit between the command and an application, and the likelihood an application is the target for the next incoming command.

Routing natural language commands to the appropriate applications

A method is given for automatically rewriting orthography of a stream of text words, for example, automatically and properly capitalizing words in the stream. If a word in the stream has an entry in an orthography rewrite lexicon, the word is automatically replaced with an orthographically rewritten form of the word from the orthography rewrite lexicon. In addition, selected words in the stream are compared to a plurality of features weighted by a maximum entropy-based algorithm, to automatically determine whether to rewrite orthography of any of the selected words.

Automatic orthographic transformation of a text stream

Features are disclosed for reducing errors in speech recognition processing. Methods for reducing errors can include receiving multiple speech recognition hypotheses based on an utterance indicative of a command or query of a user and determining a command or query within a grammar having a least amount of difference from one of the speech recognition hypotheses. The determination of the least amount of difference may be based at least in part on a comparison of individual subword units along at least some of the sequence paths of the speech recognition hypotheses and the grammar. For example, the comparison may be performed on the phoneme level instead of the word level.

Jeffrey Penrod Adams

Papers

Speech recognition power management

Wake word evaluation

Routing natural language commands to the appropriate applications

Automatic orthographic transformation of a text stream

Error reduction in speech processing