Weighted sequential variance adaptation with prior knowledge for noise robust speech recognition

A speech recognition system embodied in a digital system, the speech recognition system comprising:

• a microphone;
• a plurality of acoustic models stored in a memory of the digital system;
• a speech recognizer operatively connected to the microphone to receive a speech signal and configured to use the acoustic models to recognize speech in the speech signal;
• and
• a model adaptor subsystem operatively connected to the microphone to receive the speech signal and configured to adapt at least one acoustic model of the plurality of acoustic models by
• estimating noise in a portion of the speech signal;
• determining a first estimated variance scaling vector using an estimated 2-order polynomial and the noise estimation, wherein the estimated 2-order polynomial represents a priori knowledge of a dependency of a variance scaling vector on noise;
• determining a second estimated variance scaling vector using statistics from prior portions of the speech signal;
• determining a variance scaling factor using the first estimated variance scaling vector and the second estimated variance scaling vector;
• and
• using the variance scaling factor to adapt the at least one acoustic model.

A digital system comprising:

• a microphone;
• a memory configured to store a plurality of acoustic models and a plurality of instructions;
• and
• a processor operatively coupled to the memory and configured to receive a portion of a speech signal from the microphone and to execute the plurality of instructions to perform a method for adapting at least one acoustic model of the plurality of acoustic models, the method comprising:
• estimating noise in the portion of the speech signal;
• determining a first estimated variance scaling vector using an estimated 2-order polynomial and the noise estimation, wherein the estimated 2-order polynomial represents a priori knowledge of a dependency of a variance scaling vector on noise;
• determining a second estimated variance scaling vector using statistics from prior portions of the speech signal;
• determining a variance scaling factor using the first estimated variance scaling vector and the second estimated variance scaling vector;
• and
• using the variance scaling factor to adapt the at least one acoustic model.