Nuance Communications, Inc.

Microsoft Technology Licensing, LLC

Carl Benjamin Quillen

Naveen Parihar

William F Ganong, Iii

Dushyant Sharma

Ljubomir Milanovic

Rong Gong

Vincent Pollet

Philip Charles Woodland

Steven Hoskins

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

Embodiments improve speech data quality through training a neural network for de-noising audio enhancement. One such embodiment creates simulated noisy speech data from high quality speech data. In turn, training, e.g., deep normalizing flow training, is performed on a neural network using the high quality speech data and the simulated noisy speech data to train the neural network to create de-noised speech data given noisy speech data. Performing the training includes minimizing errors in the neural network according to at least one of (i) a decoding error of an Automatic Speech Recognition (ASR) system processing current de-noised speech data results generated by the neural network during the training and (ii) spectral distance between the high quality speech data and the current de-noised speech data results generated by the neural network during the training.

Method and system for speech enhancement

A method for removing private data from an acoustic model includes capturing speech from a large population of users, creating a text-to-speech voice from at least a portion of the large population of users, discarding speech data from a database of speech, creating text-to-speech waveforms from the text-to-speech voice and the new database of speech with the discarded speech data and generating an automatic speech recognition model using the text-to-speech waveforms.

System and method using parameterized speech synthesis to train acoustic models

Methods described herein provide functionality for automatic speech recognition (ASR). One such embodiment performs speech recognition using received speech recognition result candidates, where the received candidates were generated by performing Statistical Language Model (SLM) based speech recognition on one or more frames of audio data. In turn, such an embodiment transmits results of the speech recognition, performed using the received speech recognition result candidates, to a user device via a communications network. Results of the speech recognition are available with lower latency than pure cloud based ASR solution.

Method and system for hybrid decoding for enhanced end-user privacy and low latency

Methods described herein provide functionality for automatic speech recognition (ASR). One such embodiment performs speech recognition using received speech recognition result candidates, where the received candidates were generated by performing Statistical Language Model (SLM) based speech recognition on one or more frames of audio data. In turn, such an embodiment transmits results of the speech recognition, performed using the received speech recognition result candidates, to a user device via a communications network. Results of the speech recognition are available with lower latency than pure cloud based ASR solutions.

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Brookline, MA, United States of America

Carl Benjamin Quillen