A method for operating a voice trigger is provided. In some implementations, the method is performed at an electronic device including one or more processors and memory storing instructions for execution by the one or more processors. The method includes receiving a sound input. The sound input may correspond to a spoken word or phrase, or a portion thereof. The method includes determining whether at least a portion of the sound input corresponds to a predetermined type of sound, such as a human voice. The method includes, upon a determination that at least a portion of the sound input corresponds to the predetermined type, determining whether the sound input includes predetermined content, such as a predetermined trigger word or phrase. The method also includes, upon a determination that the sound input includes the predetermined content, initiating a speech-based service, such as a voice-based digital assistant.

Voice trigger for a digital assistant

Systems and processes are disclosed for controlling television user interactions using a virtual assistant. A virtual assistant can interact with a television set-top box to control content shown on a television. Speech input for the virtual assistant can be received from a device with a microphone. User intent can be determined from the speech input, and the virtual assistant can execute tasks according to the user's intent, including causing playback of media on the television. Virtual assistant interactions can be shown on the television in interfaces that expand or contract to occupy a minimal amount of space while conveying desired information. Multiple devices associated with multiple displays can be used to determine user intent from speech input as well as to convey information to users. In some examples, virtual assistant query suggestions can be provided to the user based on media content shown on a display.

Intelligent automated assistant for tv user interactions

Systems and processes are disclosed for handling a multi-part voice command for a virtual assistant. Speech input can be received from a user that includes multiple actionable commands within a single utterance. A text string can be generated from the speech input using a speech transcription process. The text string can be parsed into multiple candidate substrings based on domain keywords, imperative verbs, predetermined substring lengths, or the like. For each candidate substring, a probability can be determined indicating whether the candidate substring corresponds to an actionable command. Such probabilities can be determined based on semantic coherence, similarity to user request templates, querying services to determine manageability, or the like. If the probabilities exceed a threshold, the user intent of each substring can be determined, processes associated with the user intents can be executed, and an acknowledgment can be provided to the user.

Multi-command single utterance input method

A mobile electronic device has a touch sensitive screen and an accelerometer. A translator is to translate a word or phrase that is in a first human language and that is entered via a first virtual keyboard displayed on the touch sensitive screen, into a second human language. A translator is to cause the touch sensitive screen to display the translated word or phrase and a second virtual keyboard having characters in the second human language, in response to the accelerometer detecting a change in the physical orientation of the device or movement of the device. Other embodiments are also described and claimed.

Mobile device having human language translation capability with positional feedback

This is directed to processing voice inputs received by an electronic device. In particular, this is directed to receiving a voice input and identifying the user providing the voice input. The voice input can be processed using a subset of words from a library used to identify the words or phrases of the voice input. The particular subset can be selected such that voice inputs provided by the user are more likely to include words from the subset. The subset of the library can be selected using any suitable approach, including for example based on the user's interests and words that relate to those interests. For example, the subset can include one or more words related to media items selected by the user for storage on the electronic device, names of the user's contacts, applications or processes used by the user, or any other words relating to the user's interactions with the device.

User profiling for voice input processing

Systems and processes for predictive text input are provided. In one example process, a text input can be received. The text input can be associated with an input context. A frequency of occurrence of an m-gram with respect to a subset of a corpus can be determined using a language model. The subset can be associated with a context. A weighting factor can be determined based on a degree of similarity between the input context and the context. A weighted probability of a predicted text given the text input can be determined based on the frequency of occurrence of the m-gram and the weighting factor. The m-gram can include at least one word in the text input and at least one word in the predicted text.

Predictive text input

Techniques for providing semantic search of a data store are disclosed. A similarity metric of a document comprising the data store to a concept represented in a semantic model derived at least in part from a reference source that includes content not included in the data store is determined. A relevance metric of a search query to the concept is computed. The similarity metric and the relevance metric are used to determine, at least in part, a ranking of the document with respect to the search query.

Semantic search using a single-source semantic model

Systems and processes for detecting an event within natural language are provided. In one example of a process, unstructured natural language information may be received from at least one user. The presence of event information in the unstructured natural language information may be determined. In accordance with a determination that event information is present within the unstructured natural language information, a pseudo-event entry associated with that event information may be generated.

Natural language event detection

Techniques to automatically correct or complete text are disclosed. An entered text and a context data indicating a context in which the entered text is used are received. Examples of context data include additional words and/or a phrase or sentence in which the entered text occurs. A replacement candidate to replace the entered text is determined based on the entered text and the context data.

Using parts-of-speech tagging and named entity recognition for spelling correction

Using a statistical language model to make information related to a word or other content of interest available is disclosed. An indication that a word or phrase is of interest is received. A contextual meta-information associated with the word or phrase of interest as used in context is used to make information related to the word or phrase of interest available, for example, by forming a more precise query and/or filtering search results based on the contextual meta-information.

Using statistical language models for contextual lookup

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