Abstract: Methods, apparatuses, and systems are described for dynamically navigating interactive communication systems. An example method may comprise: receiving, from a user device, sound waves or audio information, the sound waves or audio information indicative of a request to initiate an interactive communication session with a communication system of a biller or merchant; interpreting, based on the sound waves or audio information, an intent of the communication session and an identity of the biller or merchant; retrieving a predetermined interaction coding associated with the biller or merchant; and initiating the interactive communication session with the communication system of the biller or merchant based on the predetermined interaction coding.

Abstract: A method, computer program product, and computing system for receiving a signal from a single microphone. A plurality of modified signals may be generated from the single microphone signal, where the plurality of modified signals include at least one of: a speaker-specific signal, an acoustic parameter-specific signal, and a speech enhanced signal. Speech processing may be performed on the plurality of modified signals.

Abstract: Methods, apparatuses, and systems are described for artificial-intelligence based techniques for programmatically generating and integrating application programming interfaces (APIs). An example method may include, in response to receiving by one or more processors, an integration data object, processing, by the one or more processors, based at least in part on an integration machine learning model, the integration data object in order to identify one or more integration features associated with the integration data object; programmatically generating, by the one or more processors, based at least in part on the one or more integration features, an application programming interface (API) model corresponding with the integration data object; and generating, by the one or more processors, an API generation data object corresponding with the API model for execution.

Abstract: A method, computer program product, and computing system for generating a three-dimensional model of at least a portion of a three-dimensional space incorporating an ACI system via a video recording subsystem of an ACI calibration platform; and generating one or more audio calibration signals for receipt by an audio recording system included within the ACI system via an audio generation subsystem of the ACI calibration platform.

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

Abstract: A method, computer program product, and computing system for generating a plurality of acoustic relative transfer functions for a plurality of audio acquisition devices of an audio recording system deployed in an acoustic environment. The plurality of acoustic relative transfer functions may be encoded into a first embedding of acoustic relative transfer functions and at least a second embedding of acoustic relative transfer functions. Information may be extracted from at least the first embedding of acoustic relative transfer functions.

Abstract: A method, computer program product, and computing system for identifying a target output token associated with an output of a machine learning model. A portion of training data corresponding to the target output token is modified with a watermark feature, thus defining watermarked training data.

Abstract: A method, computer program product, and computing system for determining a plurality of transfer functions for a plurality of corresponding segments from a reference recording and a suspect recording. A delta transfer function between the plurality of transfer functions of a pair of corresponding segments of the plurality of corresponding segments is determined. A recording comparison confidence score is generated for the pair of corresponding segments based upon, at least in part, the delta transfer function. The suspect recording is verified based upon, at least in part, the plurality of recording comparison confidence scores.

Abstract: A method, computer program product, and computing system for selecting a reference audio acquisition device from a plurality of audio acquisition devices of an audio recording system. Audio encounter information of the reference microphone may be encoded, thus defining encoded reference audio encounter information. A plurality of acoustic relative transfer functions between the reference microphone and the plurality of audio acquisition devices of the audio recording system may be generated. The encoded reference audio encounter information and a representation of the plurality of acoustic relative transfer functions may be transmitted.

Abstract: A method, computer program product, and computing system for processing audio information associated with a speech processing system and encoding a watermark in a non-disruptive portion of the audio information.

Abstract: A method, computer program product, and computing system for receiving, from a requesting party, a request to access data from a storage device. Identity information associated with the requesting party is determined. A bespoke identity-based watermark is generated for the requesting party. The bespoke identity-based watermark is encoded into the data. The watermarked data is provided to the requesting party.

Abstract: Methods, apparatuses, and systems are described for dynamically navigating interactive communication systems. An example method may comprise: receiving, from a user device, sound waves or audio information, the sound waves or audio information indicative of a request to initiate an interactive communication session with a communication system of a biller or merchant; interpreting, based on the sound waves or audio information, an intent of the communication session and an identity of the biller or merchant; retrieving a predetermined interaction coding associated with the biller or merchant; and initiating the interactive communication session with the communication system of the biller or merchant based on the predetermined interaction coding.

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: An acoustic environment profile estimation is provided for automatic speech recognition (ASR) to compensate for the acoustic behavior of an environment in which audio is collected. Examples receive an audio signal and extract spectral features and modulation features. Extracting spectral features involves determining Mel filter bank (MFB) coefficients, and extracting modulation features involves applying Fourier transforms. The spectral features and modulation features are combined, and an acoustic environment profile estimate is extracted and provided as an input to the ASR. In some examples, the acoustic environment profile estimate is realized as acoustic environment parameters, whereas in some other examples, the acoustic environment profile estimate is realized as an acoustic embedding vector.

Abstract: A method, computer program product, and computing system for synchronizing machine vision and audio is executed on a computing device and includes obtaining encounter information of a patient encounter, wherein the encounter information includes machine vision encounter information and audio encounter information. The machine vision encounter information and the audio encounter information are temporally-aligned to produce a temporarily-aligned encounter recording.

Abstract: A method, computer program product, and computing system for receiving an input speech signal. A transcription of the input speech signal may be received. A speaker embedding may be extracted from the input speech signal. Acoustic properties from the input speech signal may be extracted. An obscured transcription may be generated from the transcription, where the obscured transcription includes obscured representations of sensitive content from the transcription. An obscured speech signal may be generated based upon, at least in part, the extracted speaker embedding and the obscured transcription, where the obscured speech signal includes obscured representations of sensitive content from the input speech signal. The obscured speech signal may be augmented based upon, at least in part, the extracted acoustic properties.

Abstract: A method, computer program product, and computing system for generating an obscured speech signal from an input speech signal and an obscured transcription from a transcription of the input speech signal. A speaker embedding may be extracted from the input speech signal. A speaker embedding delta may be generated based upon, at least in part, the extracted speaker embedding and a synthetic speaker embedding. A synthetic speech signal may be generated from the obscured speech signal using the synthetic speaker embedding. A residual signal may be generated based upon, at least in part, the obscured speech signal and the speaker embedding delta. A speech processing system may be trained using the obscured transcription, the synthetic speech signal, the speaker embedding delta, and the residual signal.

Abstract: A method, computer program product, and computing system for receiving an input speech signal. A transcription of the input speech signal may be generated via an automated speech recognition (ASR) system. One or more splitting points between one or more sensitive content portions and one or more non-sensitive content portions from the transcription may be identified. The input speech signal maybe split into the one or more sensitive content portions and the one or more non-sensitive content portions based upon, at least in part, the one or more splitting points, thus defining one or more sensitive content signals and one or more non-sensitive content signals.

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, 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.