Abstract: A mixed-media ACD device is configured to monitor one or more encounter participants of a patient encounter and includes a machine vision system configured to obtain machine vision encounter information concerning the patient encounter. An audio recording system is configured to obtain audio encounter information concerning the patient encounter, wherein the audio recording system includes a plurality of discrete audio acquisition devices.

Abstract: A method, computer program product, and computer system for measuring, by a computing device, a plurality of Room Impulse Responses (RIRs) associated with a set of two or more microphones. At least a portion of the RIRs may be augmented. At least the portion of the RIRs may be converted to their respective Relative Transfer Function (RTF) representations. The RTF representations may be applied to training data to generate an acoustic model for automatic speech recognition.

Abstract: Disclosed methods and systems are directed to determining a best microphone pair and segmenting sound signals. The methods and systems may include receiving a collection of sound signals comprising speech from one or more audio sources (e.g., meeting participants) and/or background noise. The methods and systems may include calculating a TDOA and determining, based on the TDOA and via robust statistics, the best pair of microphones. The methods and systems may also include segmenting sound signals from multiple sources.

Abstract: A method, computer program product, and computing system for obtaining encounter information of a patient encounter, wherein the encounter information includes machine vision encounter information; processing the machine vision encounter information to identify one or more humanoid shapes; and steering one or more audio recording beams toward the one or more humanoid shapes to capture audio encounter information.

Abstract: A system for and method of characterizing a target application acoustic domain analyzes one or more speech data samples from the target application acoustic domain to determine one or more target acoustic characteristics, including a CODEC type and bit-rate associated with the speech data samples. The determined target acoustic characteristics may also include other aspects of the target speech data samples such as sampling frequency, active bandwidth, noise level, reverberation level, clipping level, and speaking rate. The determined target acoustic characteristics are stored in a memory as a target acoustic data profile. The data profile may be used to select and/or modify one or more out of domain speech samples based on the one or more target acoustic characteristics.

Abstract: Disclosed methods and systems are directed to determining a best microphone pair and segmenting sound signals. The methods and systems may include receiving a collection of sound signals comprising speech from one or more audio sources (e.g., meeting participants) and/or background noise. The methods and systems may include calculating a TDOA and determining, based on the TDOA and via robust statistics, the best pair of microphones. The methods and systems may also include segmenting sound signals from multiple sources.

Abstract: A method, computer program product, and computing system for acoustic speech localization, comprising receiving, via a plurality of microphones, a plurality of audio signals. Modulation properties of the plurality of audio signals may be analyzed. Speech sounds may be localized from the plurality of audio signals based upon, at least in part, the modulation properties of the plurality of audio signals.

Abstract: A method, computer program product, and computing system for acoustic speech localization, comprising receiving, via a plurality of microphones, a plurality of audio signals. Modulation properties of the plurality of audio signals may be analyzed. Speech sounds may be localized from the plurality of audio signals based upon, at least in part, the modulation properties of the plurality of audio signals.

Abstract: Disclosed methods and systems are directed to determining a best microphone pair and segmenting sound signals. The methods and systems may include receiving a collection of sound signals comprising speech from one or more audio sources (e.g., meeting participants) and/or background noise. The methods and systems may include calculating a TDOA and determining, based on the TDOA and via robust statistics, the best pair of microphones. The methods and systems may also include segmenting sound signals from multiple sources.

Abstract: A method, computer program product, and computing system for determining a time delay between a first audio signal received on a first audio detection system and a second audio signal received on a second audio detection system. The first and second audio detection systems are located within a monitored space. The first audio detection system is located with respect to the second audio detection system within the monitored space.

Abstract: A method, computer program product, and computing system for initially aligning two or more audio signals to address coarse temporal misalignment between the two or more audio signals. The two or more audio signals are detected by two or more audio detection systems within a monitored space. The two or more audio signals are subsequently realigned to address ongoing temporal signal drift between the two or more audio signals.

Abstract: A method, computer program product, and computing system for obtaining encounter information of a patient encounter, wherein the encounter information includes machine vision encounter information; and processing the encounter information to generate an encounter transcript.

Abstract: A method, computer program product, and computing system for obtaining encounter information of a patient encounter, wherein the encounter information includes machine vision encounter information; processing the machine vision encounter information to identify one or more humanoid shapes; and steering one or more audio recording beams toward the one or more humanoid shapes to capture audio encounter information.

Abstract: A method, computer program product, and computing system for source separation is executed on a computing device and includes obtaining encounter information of a patient encounter, wherein the encounter information includes first audio encounter information obtained from a first encounter participant and at least a second audio encounter information obtained from at least a second encounter participant. The first audio encounter information and the at least a second audio encounter information are processed to eliminate audio interference between the first audio encounter information and the at least a second audio encounter information.

Abstract: A method, computer program product, and computing system for monitoring a plurality of encounter participants is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is processed to: associate at least a first portion of the encounter information with at least one known encounter participant, and associate at least a second portion of the encounter information with at least one unknown encounter participant.

Abstract: A system, method and computer program product are described for voice activity detection (VAD) within a digitally encoded bitstream. A parameter extraction module is configured to extract parameters from a sequence of coded frames from a digitally encoded bitstream containing speech. A VAD classifier is configured to operate with input of the digitally encoded bitstream to evaluate each coded frame based on bitstream coding parameter classification features to output a VAD decision indicative of whether or not speech is present in one or more of the coded frames.

Abstract: A system for and method of characterizing a target application acoustic domain analyzes one or more speech data samples from the target application acoustic domain to determine one or more target acoustic characteristics, including a CODEC type and bit-rate associated with the speech data samples. The determined target acoustic characteristics may also include other aspects of the target speech data samples such as sampling frequency, active bandwidth, noise level, reverberation level, clipping level, and speaking rate. The determined target acoustic characteristics are stored in a memory as a target acoustic data profile. The data profile may be used to select and/or modify one or more out of domain speech samples based on the one or more target acoustic characteristics.

Abstract: Disclosed methods and systems are directed to determining a best microphone pair and segmenting sound signals. The methods and systems may include receiving a collection of sound signals comprising speech from one or more audio sources (e.g., meeting participants) and/or background noise. The methods and systems may include calculating a TDOA and determining, based on the TDOA and via robust statistics, the best pair of microphones. The methods and systems may also include segmenting sound signals from multiple sources.

Abstract: A system and method for speech quality detection is included. The method may include receiving, at a computing device, a first speech signal associated with a particular user. The method may include extracting one or more short-term features from the first speech signal wherein extracting short-term features includes extracting a time frame of between 10-50 ms. The method may also include determining one or more statistics of each of the one or more short-term features from the first speech signal. The method may further include classifying the one or more statistics as belonging to one of a set of quality classes.

Abstract: Provided are methods and systems for generating Direct-to-Reverberant Ratio (DRR) estimates. The methods and systems use a null-steered beamformer to produce accurate DRR estimates across a variety of room sizes, reverberation times, and source-receiver distances. The DRR estimation algorithm uses spatial selectivity to separate direct and reverberant energy and account for noise separately. The formulation considers the response of the beamformer to reverberant sound and the effect of noise. The DRR estimation algorithm is more robust to background noise than existing approaches, and is applicable where a signal is recorded with two or more microphones, such as with mobile communications devices, laptop computers, and the like.