System and Method for Multi-Microphone Automated Clinical Documentation

Abstract

A method, computer program product, and computing system for receiving information associated with an acoustic environment. A plurality of filters may be predefined to produce a plurality of beams based upon, at least in part, the information associated with the acoustic environment. The plurality of filters may be predefined to produce a plurality of nulls based upon, at least in part, the information associated with the acoustic environment. Audio encounter information may be obtained, via one or more microphone arrays, using the plurality of beams and the plurality of nulls produced by the plurality of predefined filters.

Description

RELATED APPLICATION(S)[0001]This application claims the benefit of the following U.S. Provisional Application No. 63 / 022,269 filed on 8 May 2020, the contents of which are all incorporated herein by reference.BACKGROUND[0002]Automated Clinical Documentation (ACD) may be used, e.g., to turn transcribed conversational (e.g., physician, patient, and / or other participants such as patient's family members, nurses, physician assistants, etc.) speech into formatted (e.g., medical) reports. Such reports may be reviewed, e.g., to assure accuracy of the reports by the physician, scribe, etc.[0003]The process of capturing speech may include distant conversation automated speech recognition (DCASR). DCASR includes multiple microphone systems configured to record and recognize speech of one or more speakers (e.g., a microphone array and a single mobile microphone carried by a speaker). Conventional approaches to DCASR are subject to various challenges. For example, signal acquisition via a numbe...

AI Technical Summary

Benefits of technology

The patent describes a method for improving audio quality in a specific acoustic environment by using filters to produce beams and nulls. These filters are predefined based on information about the acoustic environment, such as the location of target speakers. By using these filters, the system can better control the audio encounter and improve the overall quality of the audio. This can be useful in situations where the acoustic environment is not well-known or where the system needs to adapt to changing conditions. The method can be implemented using microphone arrays and can be used in various computing systems.

Problems solved by technology

Conventional approaches to DCASR are subject to various challenges.

For example, signal acquisition via a number of microphone systems (e.g., one or more microphone arrays and one or more mobile microphones) may be problematic.

For example, when beamforming with single microphones, there are single channel noise reduction and de-reverberation methods that cannot be applied to multiple microphone systems.

Additionally, conventional approaches to DCASR are unable to manage the combination of enhanced signals from various devices in a room.

Conventional approaches to DCASR may encounter issues when locating where the speakers are at any given time, tracking their movements, and then identifying their identify.

This may result in less accurate text transcriptions of recorded speech.

In addition, an automated speech recognition (ASR) system, that converts the audio to text, may be unable to process the various metadata and audio signals from multiple sources (e.g., multiple microphone systems).

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