Low-power noise characterization over a distributed speech recognition channel

A distributed speech recognition system, comprising:

• a noise floor estimator to provide a noise floor estimate to a feature extractor, wherein the noise floor estimator is selectively coupled between a transform module and an analysis module of the feature extractor, and the feature extractor provides a parametric representation of the noise floor estimate;
• an encoder to encode the parametric representation of the noise floor estimate and to generate an encoded parametric representation of the noise floor estimate;
• a decoder to decode the encoded parametric representation of the noise floor estimate and to generate a decoded parametric representation of the noise floor estimate;
• and
• a noise model generator to create a statistical model of noise feature vectors based on the decoded parametric representation of the noise floor estimate.

A distributed speech recognition system, comprising:

• a noise floor estimator to provide a noise floor estimate to a feature extractor, wherein the feature extractor provides a parametric representation of the noise floor estimate;
• an encoder to encode the parametric representation of the noise floor estimate and to generate an encoded parametric representation of the noise floor estimate;
• a decoder to decode the encoded parametric representation of the noise floor estimate and to generate a decoded parametric representation of the noise floor estimate;
• a speech/noise de-multiplexer to determine whether received data includes noise;
• and
• a noise model generator to create a statistical model of noise feature vectors based on the decoded parametric representation of the noise floor estimate.

A distributed speech recognition system, comprising:

• a first processing device, including:
• a noise floor estimator to provide a noise floor estimate to a feature extractor, wherein the noise floor estimator is selectively coupled between a transform module and an analysis module of the feature extractor, and the feature extractor provides a parametric representation of the noise floor estimate,
• an encoder to compress the parametric representation of the noise floor estimate and to generate an encoded parametric representation of the noise floor estimate, and
• a front-end controller to determine when at least one of the noise floor estimator, the feature extractor, and the encoder is to be turned on or off and to determine when the noise floor estimator is to provide the noise floor estimate to the feature extractor;
• a transmitter to transmit the encoded parametric representation of the noise floor estimate;
• a receiver to receive the encoded parametric representation of the noise floor estimate from the transmitter;
• and
• a second processing device, including:
• a decoder to decompress the encoded parametric representation of the noise floor estimate and to generate a decoded parametric representation of the noise floor estimate,
• a speech/noise de-multiplexer to determine whether received data represents noise, and
• a noise model generator to create a statistical model of noise feature vectors based on the decoded parametric representation of the noise floor estimate, wherein the distributed speech recognition system utilizes an acoustic model adaptation technique.

A method of creating a statistical model of noise in a distributed speech recognition system, comprising:

• determining when to provide a noise floor estimate;
• generating a parametric representation of the noise floor estimate;
• determining whether received data includes a parametric representation of noise;
• and
• creating a statistical model of noise feature vectors based on the parametric representation of the noise floor estimate.

An article comprising:

• a storage medium having stored thereon instructions that when executed by a machine result in the following:
• determining when to provide a noise floor estimate;
• generating a parametric representation of the noise floor estimate;
• determining whether received data includes a parametric representation of noise;
• and
• creating a statistical model of noise feature vectors based on the parametric representation of the noise floor estimate.