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 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

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 user request is received from a mobile client device, where the user request includes at least a speech input and seeks an informational answer or performance of a task. A failure to provide a satisfactory response to the user request is detected. In response to detection of the failure, information relevant to the user request is crowd-sourced by querying one or more crowd sourcing information sources. One or more answers are received from the crowd sourcing information sources, and the response to the user request is generated based on at least one of the one or more answers received from the one or more crowd sourcing information sources.

Crowd sourcing information to fulfill user requests

A device can be configured to receive speech input from a user. The speech input can include a command for accessing a restricted feature of the device. The speech input can be compared to a voiceprint (e.g., text-independent voiceprint) of the user's voice to authenticate the user to the device. Responsive to successful authentication of the user to the device, the user is allowed access to the restricted feature without the user having to perform additional authentication steps or speaking the command again. If the user is not successfully authenticated to the device, additional authentication steps can be request by the device (e.g., request a password).

Device access using voice authentication

We investigate the technical challenges that are faced when making a transition from the speaker-dependent to speaker-independent speech recognition technology in mobile communication devices. Due to globalization as well as the international nature of the markets and the future applications, speaker independence implies the development and use of language-independent automatic speech recognition (ASR) to avoid logistic difficulties. We propose an architecture for embedded multilingual speech recognition systems. Multilingual acoustic modeling, automatic language identification, and on-line pronunciation modeling are the key features which enable the creation of truly language- and speaker-independent ASR applications with dynamic vocabularies and sparse implementation resources. Our experimental results confirm the viability of the proposed architecture. While the use of multilingual acoustic models degrades the recognition rates only marginally, a recognition accuracy decrease of approximately 4% is observed due to sub-optimal on-line text-to-phoneme mapping and automatic language identification. This performance loss can nevertheless be compensated by applying acoustic model adaptation techniques.

I. Viikki

Papers

Speaker- and language-independent speech recognition in mobile communication systems