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

This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our approach to training high-performance Machine Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient, high-quality training data without compromising user privacy.

Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

Smart homes equipped with ambient intelligence technology constitute a promising direction to enable the growing number of elderly to continue to live in their own home as long as possible. However, this calls for technological solutions that suit their specific needs and capabilities. The Sweet-Home project aims at developing a new user friendly technology for home automation based on voice command. This paper reports a user evaluation assessing the acceptance and fear of this new technology. Eight healthy persons between 71 and 88 years old, 7 relatives (child, grandchild or friend) and 3 professional carers participated in a user evaluation. During about 45 min, the persons were questioned in co-discovery in the Domus smart home alternating between interview and wizard of Oz periods followed by a debriefing. The experience aimed at testing four important aspects of the project: voice command, communication with the outside world, domotics system interrupting a person's activity, and electronic agenda. Voice interface appeared to have a great potential to ease daily living for elderly and frail persons and would be better accepted than more intrusive solutions. By considering still healthy and independent elderly people in the user evaluation, an interesting finding that came up is their overall acceptance provided the system does not drive them to a lazy lifestyle by taking control of everything. This particular fear must be addressed for the development of smart homes that support daily living by giving them more ability to control rather than putting them away from the daily routine.

https://hal.archives-ouvertes.fr/hal-00953242/document

Design and evaluation of a smart home voice interface for the elderly: acceptability and objection aspects

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

Disclosed herein are automated emergency detection and response systems and methods, in accordance with different embodiments of the invention. In some embodiments, respective data sets generated in respect of distinctly located sensors are compared, and an appropriate response protocol is selected based on this comparison and as a function of at least one of the data sets.

Emergency detection and response system and method

Demands on long-term-care facilities are predicted to increase at an unprecedented rate as the baby boomer generation reaches retirement age. Aging-in-place (i.e. aging at home) is the desire of most seniors and is also a good option to reduce the burden on an over-stretched long-term-care system. Personal Emergency Response Systems (PERSs) help enable older adults to age-in-place by providing them with immediate access to emergency assistance. Traditionally they operate with push-button activators that connect the occupant via speaker-phone to a live emergency call-centre operator. If occupants do not wear the push button or cannot access the button, then the system is useless in the event of a fall or emergency. Additionally, a false alarm or failure to check-in at a regular interval will trigger a connection to a live operator, which can be unwanted and intrusive to the occupant. This paper describes the development and testing of an automated, hands-free, dialogue-based PERS prototype. The prototype system was built using a ceiling mounted microphone array, an open-source automatic speech recognition engine, and a 'yes' and 'no' response dialog modelled after an existing call-centre protocol. Testing compared a single microphone versus a microphone array with nine adults in both noisy and quiet conditions. Dialogue testing was completed with four adults. The microphone array demonstrated improvement over the single microphone. In all cases, dialog testing resulted in the system reaching the correct decision about the kind of assistance the user was requesting. Further testing is required with elderly voices and under different noise conditions to ensure the appropriateness of the technology. Future developments include integration of the system with an emergency detection method as well as communication enhancement using features such as barge-in capability. The use of an automated dialog-based PERS has the potential to provide users with more autonomy in decisions regarding their own health and more privacy in their own home.

https://tspace.library.utoronto.ca/bitstream/1807/76774/1/Development%20of%20an%20automated.pdf

Development of an automated speech recognition interface for Personal Emergency Response Systems.

A system for detecting and responding to emergency events includes a plurality of local emergency detection and response units positioned in a local area. Each unit includes one or more local sensing agents and a local detection manager. Each local sensing agent is operable to detect emergency events by a change in a given emergency factor in the local area and to convey data representative of the change in the emergency factor to a detection manager which is operable to receive the data and to assign a value to the emergency factor according to the data. A central location controller unit and/or the local emergency detection and response unit are operable for classifying the assigned value of the emergency function to form an assigned value classification and for initiating the local emergency event response agent to implement a response protocol according to the assigned value classification.

Automated emergency detection and response

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Emergency detection and response system and method

Development of an automated speech recognition interface for Personal Emergency Response Systems.

Automated emergency detection and response