Abstract: There is provided a speech processing system that includes a neural encoder module. A processor that receives an audio signal; and the memory that contains instructions that control said processor to perform operations that process speech. In an implementation, a front end module can include a Neural Spatial RTF Estimator and a neural spatial and residual encoder (NSRE) configured accept as inputs a spectral encoded reference channel stream to output Neural Transfer Functions (NTFs). In another implementation, a front end module encodes and outputs a Ch1 bitstream; computes a plurality of relative transfer functions (RTFs) for an N-Channel signal and outputs an N?1 RTFs or an RTF codebook ids and computes and processes an N?1 residual stream; and a back end module comprising a neural encoder module configured to accept the RTFs and output an encoded speech signal comprising an embedding that comprises features extracted from RTFs.

Abstract: A method, computer program product, and computing system for receiving a speech signal from each microphone of a plurality of microphones, thus defining a plurality of signals. One or more noise signals associated with microphone self-noise may be received. One or more self-noise-based augmentations may be performed on the plurality of signals based upon, at least in part, the one or more noise signals associated with microphone self-noise, thus defining one or more self-noise-based augmented signals.

Abstract: A method of performing distance estimation between a first recording device at a first location and a second recording device at a second location includes: estimating acoustic relative transfer function (RTF) between the first recording device and the second recording device for a sound signal, e.g., by applying an improved proportionate normalized least mean square (IPNLMS) filter; and estimating the distance between the first recording device and the second recording device based on the RTF. The at least one acoustic feature extracted from the RTF estimated between the first recording device and the second recording device includes at least one of clarity index, direct-to-reverberant ratio (DRR), and reverberation time. A distributed-gradient-boosting algorithm with regression trees is used in combination with signal-to-reverberation ratio (SRR) and the at least one acoustic feature extracted from the RTF to estimate the distance between the first recording device and the second recording device.

Abstract: A method, computer program product, and computing system for receiving one or more inputs indicative of at least one of: a relative location of a speaker and a microphone array, and a relative orientation of the speaker and the microphone array. One or more reference signals may be received. A speech processing system may be trained using the one or more inputs and the one or more reference signals.

Abstract: A method includes: providing a workstation having a playback app configured for audio playback; providing a decryption module having a decryption functionality communicatively connected to the playback app; encrypting, by a server using an encryption key associated with the decryption module, audio data; and decrypting, using the decryption module, the encrypted audio data. The decryption module having the decryption functionality is provided as part of the playback app, as part of firmware of a headphone, or as part of a phone app. The method can additionally include: i) authenticating, using a voice biometric authentication module, a transcriber; ii) enabling decryption by the decryption module only upon input of a decode PIN by the transcriber; and iii) a) modifying the audio data to spatialize speech component and noise component of the audio data at different angles using head-related transfer function (HRTF) filtering, and b) playing back the audio data binaurally.

Abstract: A method, computer program product, and computing system for defining 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 plurality of feature coefficients of a target domain of a reference signal, thus generating a plurality of time-varying spectrally-augmented feature coefficients of the reference signal.

Abstract: A method of performing distance estimation between a first recording device at a first location and a second recording device at a second location includes: estimating acoustic relative transfer function (RTF) between the first recording device and the second recording device for a sound signal, e.g., by applying an improved proportionate normalized least mean square (IPNLMS) filter; and estimating the distance between the first recording device and the second recording device based on the RTF. The at least one acoustic feature extracted from the RTF estimated between the first recording device and the second recording device includes at least one of clarity index, direct-to-reverberant ratio (DRR), and reverberation time. A distributed-gradient-boosting algorithm with regression trees is used in combination with signal-to-reverberation ratio (SRR) and the at least one acoustic feature extracted from the RTF to estimate the distance between the first recording device and the second recording device.

Abstract: A method of performing at least de-reverberation and noise-reduction of an input sound signal of at least one input channel includes: performing, using at least one filter element, at least one of de-reverberation and noise-reduction of the input sound signal to generate a clean output sound signal; and determining, by a non-intrusive measure (NIM) estimation element, at least one non-intrusive measure (NIM) from the sound signal, wherein the at least one NIM includes at least one of voice activity detection (VAD) posterior, reverberation time, clarity index, direct-to-reverberant ratio (DRR), and signal-to-noise ratio (SNR); the de-reverberation is achieved by applying at least one channel shortening (CS) filter component of the at least one filter element in conjunction with the at least one NIM; and the noise reduction is performed in combination with the de-reverberation by the channel shortening (CS) filter component.

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 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 noise component models mapping one or more noise components 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 noise component models and first device training data.

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 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 receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. One or more inter-microphone gain-based augmentations may be performed on the plurality of signals, thus defining one or more inter-microphone gain-augmented signals.

Abstract: A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. One or more microphone frequency responses associated with at least one microphone may be received. One or more microphone frequency response-based augmentations may be performed on the plurality of signals based upon, at least in part, the one or more microphone frequency responses, thus defining one or more microphone frequency response-based augmented signals.

Abstract: A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. Harmonic distortion associated with at least one microphone may be determined. One or more harmonic distortion-based augmentations may be performed on the plurality of signals based upon, at least in part, the harmonic distortion associated with the at least one microphone, thus defining one or more harmonic distortion-based augmented signals.

Abstract: A method, computer program product, and computing system for obtaining calibration information for a three-dimensional space incorporating an ACI system; and processing the calibration information to calibrate the ACI system.

Abstract: A method, computer program product, and computing system for receiving information associated with an acoustic environment. Acoustic metadata associated with audio encounter information received by a first microphone system may be received. One or more speaker representations may be defined based upon, at least in part, the acoustic metadata associated with the audio encounter information and the information associated with the acoustic environment. One or more portions of the audio encounter information may be labeled with the one or more speaker representations and a speaker location within the acoustic environment.

Abstract: A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thus defining a plurality of signals. One or more inter-microphone gain-based augmentations may be performed on the plurality of signals, thus defining one or more inter-microphone gain-augmented signals.

Abstract: A method, computer program product, and computing system for receiving information associated with an acoustic environment. Acoustic metadata associated with audio encounter information received by a first microphone system may be received. One or more speaker representations may be defined based upon, at least in part, the acoustic metadata associated with the audio encounter information and the information associated with the acoustic environment. One or more portions of the audio encounter information may be labeled with the one or more speaker representations and a speaker location within the acoustic environment.

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.