US 11,551,670 B1Granted

Systems and methods for generating labeled data to facilitate configuration of network microphone devices

AssigneeSonos, Inc.

InventorsConnor Kristopher Smith, Kurt Thomas Soto, Charles Conor Sleith

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Abstract

Systems and methods for generating training data are described herein. Pieces of metadata captured by a plurality of networked sensor systems can be captured, where each piece of metadata is associated with a specific set of sensor data captured by one of the plurality of networked sensor systems and includes a set of characteristics for the specific set of captured sensor data. A probabilistic model can be generated based on the received metadata and simulations can be performed based upon a training corpus by generating multiple scenarios, and, for each scenario, a scenario specific version of a particular annotated sample is generated by performing a simulation using the particular annotated sample. The scenario specific versions of annotated samples from the training corpus can be stored as a training data set on the at least one network device.

Claims18

Dependent claims

A method for updating software configuration parameters of at least one network microphone device (NMD), the method comprising:

capturing a plurality of sets of sound data using a plurality of network microphone devices (NMDs), where each of the NMDs is configured in accordance with a set of NMD software configuration parameters;
for each captured set of sound data, capturing sound metadata associated with a specific set of sound data, where each piece of sound metadata comprises a set of characteristics for a set of sound data;
receiving sound metadata captured by the plurality of NMDs using at least one network device;
generating a probabilistic model based on the received sound metadata using the at least one network device, where the probabilistic model comprises a plurality of probability distribution functions (PDFs) for a characteristic from the set of characteristics;
performing a plurality of acoustic simulations to obtain a plurality of noised versions of a training data set of annotated speech samples using the at least one network device by:
generating a plurality of scenarios, where each scenario comprises a set of characteristics for a set of sound data and values for the characteristics in the set of characteristics for the scenario are drawn from the probabilistic model;
and
for each of the plurality of scenarios, generating a noised version of a specific annotated speech sample by performing an acoustic simulation based upon a specific scenario from the plurality of scenarios;
simulating performance of a set of modified NMD software configuration parameters at the at least one network device using the noised version of the training data set of annotated speech samples;
sending modified NMD software configuration parameters to at least one NMD;
and
updating the software of the at least one NMD based upon the modified NMD software configuration parameters.

9 more elements

A non-transitory machine readable medium containing processor instructions for updating software configuration parameters of at least one network microphone device (NMD), where execution of the instructions by at least one processor causes at least one computing device to perform a process that comprises:

capturing a plurality of sets of sound data using a plurality of network microphone devices (NMDs), where each of the NMDs is configured in accordance with a set of NMD software configuration parameters;
for each captured set of sound data, capturing sound metadata associated with a specific set of sound data, where each piece of sound metadata comprises a set of characteristics for a set of sound data;
receiving sound metadata captured by the plurality of NMDs using at least one network device;
generating a probabilistic model based on the received sound metadata using the at least one network device, where the probabilistic model comprises a plurality of probability distribution functions (PDFs) for a characteristic from the set of characteristics;
performing a plurality of acoustic simulations to obtain a plurality of noised versions of a training data set of annotated speech samples using the at least one network device by:
generating a plurality of scenarios, where each scenario comprises a set of characteristics for a set of sound data and values for the characteristics in the set of characteristics for the scenario are drawn from the probabilistic model;
and
for each of the plurality of scenarios, generating a noised version of a specific annotated speech sample by performing an acoustic simulation based upon a specific scenario from the plurality of scenarios;
simulating performance of a set of modified NMD software configuration parameters at the at least one network device using the noised version of the training data set of annotated speech samples;
sending modified NMD software configuration parameters to at least one NMD;
and
updating the software of the at least one NMD based upon the modified NMD software configuration parameters.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/031,769Filed 24 Sep 2020 · us provisional application
Priority: 26 Sep 2019
Granted: 10 Jan 2023
Term ends: Not estimatedThe source records don’t support an estimate.

Classification13 codes

G10L 15/063
Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice: training
G10L 15/148
Hidden Markov Models [HMMs]: duration modelling in HMMs, e.g. semi HMM, segmental models or transition probabilities
G10L 15/16
Speech classification or search: using artificial neural networks
G10L 15/22
Speech recognition: procedures used during a speech recognition process, e.g. man-machine dialogue