Nuance Communications, Inc.

Microsoft Technology Licensing, LLC

Dushyant Sharma

Patrick A Naylor

Daniel Paulino Almendro Barreda

Uwe Helmut Jost

Ljubomir Milanovic

Donald E Owen

Rong Gong

William F Ganong, Iii

Joel Praveen Pinto

James W Fosburgh

Stanislav Kruchinin

Daniel Almendro Barreda

Pablo Peso Parada

Guido Remi Marcel Gallopyn

Sridhar Pilli

Jose Lainez

Francesco Nespoli

Amr Nour-Eldin

Philipp Salletmayr

Do Yeong Kim

Mahesh Godavarti

Sharmistha Sarkar Gray

Carl Benjamin Quillen

Junho Park

Daniel T Jones

Shou-Chun Yin

Sam Karimian-Azari

DoYeong Kim

Jose E G Lainez

A method, computer program product, and computing system for estimating noise spectrum from a target audio signal segment. An acoustic neural embedding is generated from the target audio signal segment. An augmented audio signal segment is generated with background acoustic properties of the target audio signal segment by processing an input audio signal segment with the noise spectrum and the acoustic neural embedding using a neural network.

System and method for replicating background acoustic properties using neural networks

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.

Data augmentation system and method for multi-microphone systems

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.

Multi-channel speech compression system and method

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.

System and method for single channel distant speech processing

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.

System and method for acoustic channel identification-based data verification

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.

System and method for secure transcription generation

A method, computer program product, and computing system for receiving a plurality of signals from a plurality of microphones, thus defining a plurality of channels. A weighted multichannel representation of the plurality of channels may be generated. A plurality of weights for each channel of the plurality of channels may be generated based upon, at least in part, the weighted multichannel representation of the plurality of channels. A single channel representation of the plurality of channels may be generated based upon, at least in part, the weighted multichannel representation of the plurality of channels and the plurality of weights generated for each channel of the plurality of channels.

System and method for self-attention-based combining of multichannel signals for speech processing

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.

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.

System and method for watermarking audio data for automated speech recognition (ASR) systems

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.

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.

System and method for secure training of speech processing systems

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.

Method for neural beamforming, channel shortening and noise reduction

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.

System and method for data augmentation of feature-based voice data

A method of processing speech includes: providing a first set of audio data having audio features in a first bandwidth; down-sampling the first set of audio data to a second bandwidth lower than the first bandwidth; producing, by a high frequency reconstruction network (HFRN), an estimate of audio features in the first bandwidth for the first set of audio data, based on at least the down-sampled audio data; inputting, into the HFRN, a second set of audio data having audio features in the second bandwidth; producing, by the HFRN, based on a second set of audio data having audio features in the second bandwidth, an estimate of audio features in the first bandwidth for the second set of audio data; and training a speech processing system (SPS) using the estimates of audio features in the first bandwidth for the first and second sets of audio data.

Feature domain bandwidth extension and spectral rebalance for ASR data augmentation

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.

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Woburn, MA, United States of America

Dushyant Sharma