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

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

An electronic device may capture a voice command from a user. The electronic device may store contextual information about the state of the electronic device when the voice command is received. The electronic device may transmit the voice command and the contextual information to computing equipment such as a desktop computer or a remote server. The computing equipment may perform a speech recognition operation on the voice command and may process the contextual information. The computing equipment may respond to the voice command. The computing equipment may also transmit information to the electronic device that allows the electronic device to respond to the voice command.

Electronic Devices with Voice Command and Contextual Data Processing Capabilities

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

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

Determining the end of an utterance for purposes of automatic speech recognition (ASR) may be improved with a system that provides early results and/or incorporates semantic tagging. Early ASR results of an incoming utterance may be prepared based at least in part on an estimated endpoint and processed by a natural language understanding (NLU) process while final results, based at least in part on a final endpoint, are determined. If the early results match the final results, the early NLU results are already prepared for early execution. The endpoint may also be determined based at least in part on the content of the utterance, as represented by semantic tagging output from ASR processing. If the tagging indicate completion of a logical statement, an endpoint may be declared, or a threshold for silent frames prior to declaring an endpoint may be adjusted.

Enhanced endpoint detection for speech recognition

Features are disclosed for managing the use of speech recognition models and data in automated speech recognition systems. Models and data may be retrieved asynchronously and used as they are received or after an utterance is initially processed with more general or different models. Once received, the models and statistics can be cached. Statistics needed to update models and data may also be retrieved asynchronously so that it may be used to update the models and data as it becomes available. The updated models and data may be immediately used to re-process an utterance, or saved for use in processing subsequently received utterances. User interactions with the automated speech recognition system may be tracked in order to predict when a user is likely to utilize the system. Models and data may be pre-cached based on such predictions.

Speech model retrieval in distributed speech recognition systems

Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (“ASR”) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (“GMMs”) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

Automatic speaker identification using speech recognition features

An efficient audio streaming method and apparatus includes a client process implemented on a client or local device and a server process implemented on a remote server or server(s). The client process and server process each have speech recognition components and communicate over a network, and together efficiently manage the detection of speech in an audio signal streamed by the local device to the server for speech recognition and potentially further processing at the server. The client process monitors audio input and in a first detection stage, implements endpointing on the local device to determine when speech is detected. The client process may further determine if a “wakeword” is detected, and then the client process opens a connection and begins streaming audio to the server process via the network. The server process receives the speech audio stream and monitors the audio, implementing endpointing in the server process, to determine when to tell the client process to close the connection and stop streaming audio. The client process continues streaming audio to the server until the server process determines disconnect criteria have been met and tells the client process to stop streaming audio.

Hugh Evan Secker-Walker

Papers

Speech recognition power management

Enhanced endpoint detection for speech recognition

Speech model retrieval in distributed speech recognition systems

Automatic speaker identification using speech recognition features

Distributed endpointing for speech recognition