Techniques for improved audio processing using combinations of clipping engines and acoustic models

Abstract

A system and method for audio processing. A method includes training an acoustic model over a plurality of training iterations by, at each of the plurality of training iterations: applying the acoustic model to features extracted from training audio data in order to output a set of acoustic model predictions; applying a language model to at least a set of key term sample data in order to output a set of language model predictions, wherein the key term sample data demonstrates use of a plurality of key terms; clipping the training audio data into a plurality of clips based on the acoustic model predictions and the language model predictions; and tuning the acoustic model via a machine learning algorithm using the plurality of clips.