Abstract: A method, computer program product, and computing system for receiving input content that may include sensitive information for processing by a machine learning model; processing the input content with the machine learning model to generate output content; processing the output content to determine if the output content includes any sensitive information; and if the output content includes any sensitive information, scrutinizing the input content to determine if the input content supports the inclusion of the sensitive information in the output content.

Abstract: A method, computer program product, and computing system for obtaining one or more speech signals from a first device, thus defining one or more first device speech signals. One or more speech signals may be obtained from a second device, thus defining one or more second device speech signals. One or more acoustic relative transfer functions mapping reverberation from the one or more first device speech signals to the one or more second device speech signals may be generated. One or more augmented second device speech signals may be generated based upon, at least in part, the one or more acoustic relative transfer functions and first device training data.

Abstract: A method, computer program product, and computing system for non-intrusive speech intelligibility estimation. A degraded audio signal is processed in a pretrained automatic speech recognition (ASR) system; ASR encoder features of the degraded audio signal are generated; the ASR encoder features are processed to identify patterns in the ASR encoder features; patterns that represent levels of intelligibility of the audio signal are recognized; and a predicted intelligibility of the audio signal based on the recognized patterns of the ASR encoder features is determined.

Abstract: A method, computer program product, and computing system for encoding audio encounter information of a reference audio acquisition device of a plurality of audio acquisition devices of an audio recording system, thus defining encoded reference audio encounter information. Location information may be estimated, via a machine vision system, for an acoustic source within an acoustic environment. One or more acoustic relative transfer functions may be selected from a plurality of acoustic relative transfer functions for the plurality of audio acquisition devices of the audio recording system based upon, at least in part, the location information. The encoded reference audio encounter information and a representation of the selected one or more acoustic relative transfer function may be transmitted.

Abstract: A method, computer program product, and computing system for obtaining one or more speech signals from a first device, thus defining one or more first device speech signals. One or more speech signals may be obtained from a second device, thus defining one or more second device speech signals. An acoustic relative transfer function may be selected from a plurality of acoustic relative transfer functions based upon, at least in part, the one or more first device speech signals and the one or more second device speech signals. The one or more second device speech signals may be augmented, at run-time, based upon, at least in part, the acoustic relative transfer function.

Abstract: A method, computer program product, and computing system for encoding audio encounter information of a reference audio acquisition device of a plurality of audio acquisition devices of an audio recording system, thus defining encoded reference audio encounter information. Location information may be estimated, via a machine vision system, for an acoustic source within an acoustic environment. One or more acoustic relative transfer functions may be selected from a plurality of acoustic relative transfer functions for the plurality of audio acquisition devices of the audio recording system based upon, at least in part, the location information. The encoded reference audio encounter information and a representation of the selected one or more acoustic relative transfer function may be transmitted.

Abstract: A method, computer program product, and computing system for generating a plurality of acoustic relative transfer functions associated with a plurality of audio acquisition devices of an audio recording system deployed in an acoustic environment. Acoustic relative transfer functions of at least a pair of audio acquisition devices of the plurality of audio acquisition devices may be compared. Location information associated with an acoustic source within the acoustic environment may be determined based upon, at least in part, the comparison of the acoustic relative transfer functions of the at least a pair of audio acquisition devices of the plurality of audio acquisition devices.

Abstract: A method, computer program product, and computing system for securely transmitting voice signals. A speech signal including a content component and a speaker component of a first voice is received at an encoder. The speaker component of the speech signal is processed, using machine learning, to generate a speaker embedding. The content component of the voice signal is processed, using machine learning and based at least on the speaker embedding, to generate a content embedding having minimized speaker information. The content embedding is transmitted to a decoder for restoring the received speech signal.

Abstract: A method, computer program product, and computing system for secure speech feature extraction. A speech signal comprising content information and speaker information is received and a component of the speaker information is altered to generate an augmented voice signal. In a first neural network, first embeddings of the received voice signal are generated. In a second neural network, second embeddings of the received voice signal having minimized speaker information based on the augmented voice signal are generated. The second neural network is trained to generate the second embeddings to be similar to the first embeddings generated by the first neural network.

Abstract: A method, computer program product, and computing system for generating a plurality of acoustic relative transfer functions associated with a plurality of audio acquisition devices of an audio recording system deployed in an acoustic environment. Acoustic relative transfer functions of at least a pair of audio acquisition devices of the plurality of audio acquisition devices may be compared. Location information associated with an acoustic source within the acoustic environment may be determined based upon, at least in part, the comparison of the acoustic relative transfer functions of the at least a pair of audio acquisition devices of the plurality of audio acquisition devices.

Abstract: A method, computer program product, and computing system for disentangling background information from speaker information in a speech signal. Background information is extracted from the speech signal to generate a background acoustics embedding and speaker information is extracted from the speech signal to generate a speaker acoustics embedding. A first loss factor is applied to the background acoustics embedding to decrease speaker information therein to generate a processed background acoustics embedding using machine learning and a second loss factor is applied to the speaker acoustics embedding to decrease background information therein to generate a processed speaker acoustics embedding using machine learning. At least one of the processed background acoustics embedding and the processed speaker acoustics embedding is output to a speech processing system.

Abstract: A method, computer program product, and computing system for obtaining one or more speech signals from a first device, thus defining one or more first device speech signals. One or more speech signals may be obtained from a second device, thus defining one or more second device speech signals. A noise component model may be selected from a plurality of noise component models based upon, at least in part, the one or more first device speech signals and the one or more second device speech signals. The one or more second device speech signals may be augmented, at run-time, based upon, at least in part, the noise component model.

Abstract: A method, computer program product, and computing system for receiving feature-based voice data associated with a first acoustic domain. One or more reverberation-based augmentations may be performed on at least a portion of the feature-based voice data, thus defining reverberation-augmented feature-based voice data.

Abstract: A method, computer program product, and computing system for obtaining machine vision encounter information using one or more machine vision systems. Audio encounter information may be obtained using a plurality of audio acquisition devices of an audio recording system. The audio encounter information may be encoded using an audio codec. The encoding of the audio encounter information by the audio codec may be adapted based upon, at least in part, the machine vision encounter information.

Abstract: A method, computer program product, and computing system for generating a plurality of acoustic relative transfer functions associated with a plurality of audio acquisition devices of an audio recording system deployed in an acoustic environment. At least a pair of the plurality of acoustic relative transfer functions from time frames may be compared. A change in the acoustic environment may be detected based upon, at least in part, the comparison of the plurality of acoustic relative transfer functions from at least the pair of time frames.

Abstract: A method, computer program product, and computing system for generating a plurality of acoustic relative transfer functions between a plurality of audio acquisition devices of an audio recording system based upon, at least in part, one or more of a predefined speech processing application and a predefined acoustic environment. An acoustic relative transfer function codebook may be generated using the plurality of acoustic relative transfer functions. One or more channels from the plurality of audio acquisition devices of the audio recording system may be encoded using the acoustic relative transfer function codebook.

Abstract: A method, computer program product, and computing system for defining a model representative of a plurality of acoustic variations to a speech signal, thus defining a plurality of time-varying spectral modifications. The plurality of time-varying spectral modifications may be applied to a reference signal using a filtering operation, thus generating a time-varying spectrally-augmented signal.

Abstract: A method, computer program product, and computing system for receiving input content that may include sensitive information for processing by a machine learning model; processing the input content with the machine learning model to generate output content; processing the output content to determine if the output content includes any sensitive information; and if the output content includes any sensitive information, scrutinizing the input content to determine if the input content supports the inclusion of the sensitive information in the output content.

Abstract: A method, computer program product, and computing system for encoding audio encounter information of a reference audio acquisition device of a plurality of audio acquisition devices of an audio recording system, thus defining encoded reference audio encounter information. Location information may be estimated, via a machine vision system, for an acoustic source within an acoustic environment. One or more acoustic relative transfer functions may be selected from a plurality of acoustic relative transfer functions for the plurality of audio acquisition devices of the audio recording system based upon, at least in part, the location information. The encoded reference audio encounter information and a representation of the selected one or more acoustic relative transfer function may be transmitted.

Abstract: A method, computer program product, and computing system for receiving feature-based voice data. One or more data augmentation characteristics may be received. One or more augmentations of the feature-based voice data may be generated, via a machine learning model, based upon, at least in part, the feature-based voice data and the one or more data augmentation characteristics.