Sensory, Inc.

Rosetta Stone, Ltd.

Bryan Pellom

Todd F Mozer

Karl Ridgeway

Aaron M Simmons

Pieter J Vermeulen

Kadri Hacioglu

Matthew Wilder

John-Paul Hosom

Andreas Hagen

Gordon Haupt

Karl F Ridgeway

Smart listening modes for supporting quasi always-on listening on an electronic device are provided. In one embodiment, the electronic device can determine that a user is likely to utter a voice trigger in order to access the always-on listening functionality of the electronic device. In response to this determination, the electronic device can automatically enable the always-on listening functionality. Similarly, the electronic device can determine that a user is no longer likely to utter the voice trigger in order to access the always-on listening functionality of the electronic device. In response to this second determination, the electronic device can automatically disable the always-on listening functionality.

Smart listening modes supporting quasi always-on listening

Techniques for unobtrusively verifying the identity of a user of a computing device are provided. In one embodiment, the computing device can establish one or more verification models for verifying the user's identity, where at least a subset of the one or more verification models is based on enrollment data that is collected in an unobtrusive manner from the user. The computing device can then verify the user's identity using the one or more verification models.

Unobtrusive verification of user identity

Techniques for performing low-fidelity always-on A/V monitoring are provided. In one embodiment, an always-on A/V monitoring system can record audio or video footage of an area of interest on a continuous basis while operating in a low-fidelity recording mode, where the recorded audio or video footage has a quality level that is sufficient to detect one or more events that have meaning to the system or a user, but is insufficient to recognize details with respect to the area of interest that would be considered private to an individual appearing in, or associated with, the recorded audio or video footage.

Low-fidelity always-on audio/video monitoring

Techniques for implementing voice-based liveness verification are provided. In one embodiment, a computing device can present a series of challenge prompts to a user being authenticated, where each challenge prompt corresponds to a request to utter a liveness passphrase that is randomly selected from a set of liveness passphrases that have been previously enrolled by an enrolled user of the computing device. The computing device can then receive utterances from the user in response to the series of challenge prompts and, if each utterance matches its corresponding enrolled liveness passphrase, can conclude that the user is a live subject.

Voice-based liveness verification

A non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code causes the processor to receive a request from a user of a client device to initiate a speech recognition engine for a web page displayed at the client device. In response to the request, the code causes the processor to (1) download, from a server associated with a first party, the speech recognition engine into the client device; and then (2) analyze, using the speech recognition engine, content of the web page including text in an identified language to produce analyzed content based on the identified language, where the content of the web page is received from a server associated with a second party. The code further causes the processor to send a signal to cause the client device to present the analyzed content to the user at the client device.

Method and system for reading fluency training

Techniques for implementing unobtrusive training for speaker verification are provided. In one embodiment, an electronic device can receive a plurality of voice samples uttered by one or more users as they interact with a voice command-and-control feature of the electronic device and, for each voice sample, assign the voice sample to one of a plurality of voice type categories. The electronic device can further group the voice samples assigned to each voice type category into one or more user sets, where each user set comprises voice samples likely to have been uttered by a unique user. The electronic device can then, for each user set: (1) generate a voice model, (2) issue, to the unique user, a request to provide an identity or name, and (3) label the voice model with the identity or name provided by the unique user.

Unobtrusive training for speaker verification

Techniques for analyzing long-term audio recordings are provided. In one embodiment, a computing device can record audio captured from an environment of a user on a long-term basis (e.g., on the order of weeks, months, or years). The computing device can store the recorded audio on a local or remote storage device. The computing device can then analyze the recorded audio based one or more predefined rules and can enable one or more actions based on that analysis.

Analysis of long-term audio recordings

Techniques for performing continuous enrollment for face verification are provided. In one embodiment, a computing device can receive, from a user, an indication that the user wishes to authenticate himself/herself with the computing device via face verification. In response to the indication, the computing device can capture, using a camera, a series of images of the user's face and can authenticate the user by evaluating each of the series of images against a face template for the user, where the user is authenticated based on an N-th image in the series. Once the user has been authenticated, the computing device can select one or more images from the series prior to the N-th image and can add the selected images to the user's face template.

Continuous enrollment for face verification

Techniques for implementing face-based authentication with situational adaptivity are provided. In one embodiment, a computing device can create an enrollment template for a user, the enrollment template being derived from one or more enrollment images of the user's face and being usable by a face-based authentication system to authenticate the user's identity. The computing device can further determine a first set of metadata associated with the enrollment image(s) and can store the first set of metadata with the enrollment template. At a later time (e.g., an authentication event), the computing device can capture an input image of the user's face, determine a second set of metadata associated with the input image, and calculate a computational distance between the input image and the enrollment template, the calculating taking into account a degree of difference between the first and second sets of metadata. Finally, the user can be authenticated based on the distance.

Face-based authentication with situational adaptivity

In the field of language learning systems, proper pronunciation of words and phrases is an integral aspect of language learning, determining the proximity of the language learner's pronunciation to a standardized, i.e. 'perfect', pronunciation is utilized to guide the learner from imperfect toward perfect pronunciation. In this regard, a phoneme lattice scoring system is utilized, whereby an input from a user is transduced into the perfect pronunciation example in a phoneme lattice. The cost of this transduction may be determined based on a summation of substitutions, deletions and insertions of phonemes needed to transducer from the input to the perfect pronunciation of the utterance.

Growing community of inventors

Erie, CO, United States of America

Bryan Pellom