Abstract: Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Abstract: Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.

Abstract: Embodiments herein relate to ear-wearable stress and anxiety monitoring systems, devices and methods. Embodiments herein further relate to ear-wearable systems and devices that can detect and take actions to alleviate device wearer's stress and anxiety. In an embodiment an ear-wearable stress and/or anxiety monitoring system is included having a control circuit, a microphone, and a sensor package that can include a motion sensor. The ear-wearable system is configured to evaluate data from at least one of the microphone and the sensor package and classify a stress level of a device wearer using a machine learning classification model and periodically update the machine learning classification model based on indicators of stress experienced by the device wearer.

Abstract: A system and method of automatic transcription using a visual display device and an ear-wearable device. The system is configured to process an input audio signal at the display device to identify a first voice signal and a second voice signal from the input audio signal. A representation of the first voice signal and the second voice signal can be displayed on the display device and input can be received comprising the user selecting one of the first voice signal and the second voice signal as a selected voice signal. The system is configured to convert the selected voice signal to text data and display a transcript on the display device. The system can further generate an output signal sound at the first transducer of the ear-wearable device based on the input audio signal.

Abstract: Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Abstract: Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.

Abstract: Embodiments herein relate to ear-wearable devices that can be used to control other device and related methods. In a first aspect, an ear-wearable device is included having a control circuit, a wireless communications circuit, a microphone, and a motion sensor. The ear-wearable device is configured to receive an input from a wearer of the ear-wearable device, select a controllable device to be controlled based on factors including at least one of physical proximity between the ear-wearable device and the controllable device and a direction that the head of the wearer is pointing, and issue a control command to the controllable device. Other embodiments are also included herein.

Abstract: A hearing assisting system is included having a first microphone and a second microphone. The system further includes a vision device having a display device, where the display device is configured to display visual information to the user when the user is wearing the vision device. The system is further configured to process audio signals to identify first audio content information and first audio direction information related to the first audio content information. The system further can transmit, to the vision device, first display information to cause the vision device to display a non-transcript content representation of the first audio content information and a direction representation of the first audio direction information using the display device.

Abstract: Disclosed herein, among other things, are systems and methods for person-to-person voice communication between ear-wearable devices. A method includes receiving, using a microphone of a first hearing device configured to be worn on or in an ear of the first user, a first acoustic own voice signal from the first user. The method further includes transmitting, from the first hearing device via a wireless connection to a second hearing device configured to be worn on or in an ear of a second user, a first audio packet based on the received first acoustic own voice signal. The method also includes receiving, at the second hearing device via the wireless connection, the first audio packet from the first hearing device, and playing, at the second hearing device using a second receiver, an output signal for the second user based on the first audio packet.

Abstract: Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.

Abstract: Embodiments herein relate to ear-worn devices that can be used to locate other devices such as mobile electronic devices. In a first aspect, an ear-worn device is included having a control circuit, a microphone in electrical communication with the control circuit, an electroacoustic transducer for generating sound in electrical communication with the control circuit, a motion sensor in electrical communication with the control circuit, and a power supply circuit in electrical communication with the control circuit, wherein the ear-worn device is configured to issue a location command to a mobile electronic device and wherein the location command triggers the mobile electronic device to emit a locating signal. Other embodiments are also included herein.

Abstract: Embodiments herein relate to audio systems including an ear-worn device and providing for remote audio stream management. In an embodiment, an audio system is included having an ear-worn device. The audio system can be configured to receive a notification regarding availability of a remote audio stream, query a wearer of the ear-worn device regarding the remote audio stream, receive an input from the wearer of the ear-worn device regarding the remote audio stream, selectively receive the remote audio stream, and selectively play the remote audio stream through the electroacoustic transducer. Other embodiments are also included herein.

Abstract: Embodiments herein relate to medical device systems including ear-worn devices. In an embodiment, a medical device system includes an ear-worn device including a control circuit, a microphone in electrical communication with the control circuit, an electroacoustic transducer for generating sound in electrical communication with the control circuit, and a power supply circuit in electrical communication with the control circuit. The ear-worn device can be configured to received signals from a separate medical device and generate a notification using the received signals. Other embodiments are also included herein.

Abstract: Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Abstract: A system and method of automatic transcription using a visual display device and an ear-wearable device. The system is configured to process an input audio signal at the display device to identify a first voice signal and a second voice signal from the input audio signal. A representation of the first voice signal and the second voice signal can be displayed on the display device and input can be received comprising the user selecting one of the first voice signal and the second voice signal as a selected voice signal. The system is configured to convert the selected voice signal to text data and display a transcript on the display device. The system can further generate an output signal sound at the first transducer of the ear-wearable device based on the input audio signal.

Abstract: Embodiments herein relate to ear-wearable devices that can be used to control other device and related methods. In a first aspect, an ear-wearable device is included having a control circuit, a wireless communications circuit, a microphone, and a motion sensor. The ear-wearable device is configured to receive an input from a wearer of the ear-wearable device, select a controllable device to be controlled based on factors including at least one of physical proximity between the ear-wearable device and the controllable device and a direction that the head of the wearer is pointing, and issue a control command to the controllable device. Other embodiments are also included herein.

Abstract: Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Abstract: A system and method of automatic transcription using a visual display device and an ear-wearable device. The system is configured to process an input audio signal at the display device to identify a first voice signal and a second voice signal from the input audio signal. A representation of the first voice signal and the second voice signal can be displayed on the display device and input can be received comprising the user selecting one of the first voice signal and the second voice signal as a selected voice signal. The system is configured to convert the selected voice signal to text data and display a transcript on the display device. The system can further generate an output signal sound at the first transducer of the ear-wearable device based on the input audio signal.

Abstract: A wearable apparatus includes a wearable electronic device comprising a rechargeable power source, charging circuitry, a processor, and a display. A strap arrangement is connected to the wearable electronic device and comprises one or more charge ports. Each charge port is electrically coupled to the wearable electronic device and comprises a volume of resilient material comprising a through-hole dimensioned to receive one of the electronic devices. The volume of resilient material is configured to stretch and generate a retentive force sufficient to retain the electronic device within the through-hole. Electrical contacts are disposed on a wall of the volume of resilient material and recessed within a thickness of the volume of resilient material. The electrical contacts are configured to electrically communicate with corresponding electrical contacts of the electronic device.

Abstract: Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.