Noisy student teacher training for robust keyword spotting

A method implemented by one or more processors, the method comprising:

• receiving audio data capturing a spoken utterance which includes one or more keywords;
• processing the audio data using a keyword spotting (“KWS”) model to generate keyword output, wherein training the KWS model comprises:
• training an initial KWS teacher model portion of the KWS model using a labeled training data set which includes input audio features and supervised output features;
• generating augmented audio data, wherein generating the augmented audio data comprises augmenting an instance of base audio data;
• processing the augmented audio data using the initial KWS teacher model to generate a soft label;
• processing the augmented audio data using a KWS student model portion of the KWS model to generate student output;
• and
• updating one or more portions of the KWS student model portion of the KWS model based on comparing the soft label and the generated student output;
• and
• determining whether the one or more keywords are present in the spoken utterance based on the keyword output.

A method implemented by one or more processors, the method comprising:

• training an initial teacher model using a labeled training data set which includes input audio features and supervised output features;
• generating augmented audio data, wherein generating the augmented audio data comprises augmenting an instance of base audio data using time masking and/or frequency masking of the base audio data;
• processing the augmented audio data using the initial teacher model to generate a soft label;
• processing the augmented audio data using a student model to generate student output;
• and
• updating one or more portions of the student model based on comparing the soft label and the generated student output.