An information transmission device according to the present disclosure includes: an acoustic feature calculator that calculates an acoustic feature of a spoken voice; a speaker feature calculator that calculates a speaker feature from the acoustic feature using a deep neural network (DNN), the speaker feature being a feature unique to a speaker of the spoken voice; an analyzer that analyzes condition information indicating a condition to be used in calculating the speaker feature, based on the spoken voice; and an information transmitter that transmits the speaker feature and the condition information to an information reception device that performs speaker recognition processing on the spoken voice, as information to be used by the information reception device to recognize the speaker of the spoken voice.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

Systems, devices, and methods including: capturing, by a capture device, an audio and corresponding location metadata associated with an emergency call; refining the location metadata to provide a refined location metadata; correlating, by the capture device, the refined location metadata of the emergency call with a geofenced location of the computing devices of one or more first responders (FRs); screening, by the capture device or the computing device, the emergency call data; transmitting, by the capture device, a first signal to the one or more computing devices based on the correlation, the transmitted signal including a portion of the captured audio and corresponding location metadata; receiving, by the capture device, an accept signal from the one or more computing devices of one or more FRs; transmitting, by the capture device, a second signal to the one or more computing devices based on the received accept signal.

Systems, devices, and methods including: capturing, by a capture device, an audio and corresponding location metadata associated with an emergency call; refining the location metadata to provide a refined location metadata; correlating, by the capture device, the refined location metadata of the emergency call with a geofenced location of the computing devices of one or more first responders (FRs); screening, by the capture device or the computing device, the emergency call data; transmitting, by the capture device, a first signal to the one or more computing devices based on the correlation, the transmitted signal including a portion of the captured audio and corresponding location metadata; receiving, by the capture device, an accept signal from the one or more computing devices of one or more FRs; transmitting, by the capture device, a second signal to the one or more computing devices based on the received accept signal.

Estimation accuracies of a conversation satisfaction and a speech satisfaction are improved. A learning data storage unit (10) stores learning data including a conversation voice containing a conversation including a plurality of speeches, a correct answer value of a conversation satisfaction for the conversation, and a correct answer value of a speech satisfaction for each speech included in the conversation. A model learning unit (13) learns a satisfaction estimation model using a feature quantity of each speech extracted from the conversation voice, the correct answer value of the speech satisfaction, and the correct answer value of the conversation satisfaction, the satisfaction estimation model configured by connecting a speech satisfaction estimation model part that receives a feature quantity of each speech and estimates the speech satisfaction of each speech with a conversation satisfaction estimation model part that receives at least the speech satisfaction of each speech and estimates the conversation satisfaction.

Estimation accuracies of a conversation satisfaction and a speech satisfaction are improved. A learning data storage unit (10) stores learning data including a conversation voice containing a conversation including a plurality of speeches, a correct answer value of a conversation satisfaction for the conversation, and a correct answer value of a speech satisfaction for each speech included in the conversation. A model learning unit (13) learns a satisfaction estimation model using a feature quantity of each speech extracted from the conversation voice, the correct answer value of the speech satisfaction, and the correct answer value of the conversation satisfaction, the satisfaction estimation model configured by connecting a speech satisfaction estimation model part that receives a feature quantity of each speech and estimates the speech satisfaction of each speech with a conversation satisfaction estimation model part that receives at least the speech satisfaction of each speech and estimates the conversation satisfaction.

Implementations relate to techniques for providing context-dependent search results. A computer-implemented method includes receiving an audio stream at a computing device during a time interval, the audio stream comprising user speech data and background audio, separating the audio stream into a first substream that includes the user speech data and a second substream that includes the background audio, identifying concepts related to the background audio, generating a set of terms related to the identified concepts, influencing a speech recognizer based on at least one of the terms related to the background audio, and obtaining a recognized version of the user speech data using the speech recognizer.

Implementations relate to techniques for providing context-dependent search results. A computer-implemented method includes receiving an audio stream at a computing device during a time interval, the audio stream comprising user speech data and background audio, separating the audio stream into a first substream that includes the user speech data and a second substream that includes the background audio, identifying concepts related to the background audio, generating a set of terms related to the identified concepts, influencing a speech recognizer based on at least one of the terms related to the background audio, and obtaining a recognized version of the user speech data using the speech recognizer.

Some implementations involve determining a noise metric and/or a speech intelligibility metric and determining a compensation process corresponding to the noise metric and/or the speech intelligibility metric. The compensation process may involve altering a processing of audio data and/or applying a non-audio-based compensation method. In some examples, altering the processing of the audio data does not involve applying a broadband gain increase to the audio signals. Some examples involve applying the compensation process in an audio environment. Other examples involve determining compensation metadata corresponding to the compensation process and transmitting an encoded content stream that includes encoded compensation metadata, encoded video data and encoded audio data from a first device to one or more other devices.

Controlled-environment facility resident behavioral and/or health monitoring may employ controlled-environment facility resident wearables each having a band configured to be affixed around a portion of a controlled-environment facility resident, irremovable by the resident and may include sensor(s) configured to measure biometric(s) of the controlled-environment facility resident and one or more physical parameter(s) experienced by the wearable, with a transmitter transmitting the biometric(s) and/or the physical parameter(s) to a controlled-environment facility management system. The controlled-environment facility management system may predetermine one or more normal input levels of the biometric(s) and/or physical parameter(s), receive the transmitted biometric(s) and/or physical parameter(s), determine whether received biometric(s) and/or physical parameter(s) rises above or falls below the predetermined normal input level(s), and alert controlled-environment facility personnel and/or law enforcement when received physical parameter(s) and/or received biometric(s) rise above or fall below the predetermined normal input level(s).