3. The method of claim 1 , wherein said generating combined boundary posteriors includes: feeding the auditory attention features into a machine learning algorithm of a first boundary classifier to output a first set of boundary posteriors; feeding the phone posteriors into a machine learning algorithm of a second boundary classifier to output a second set of boundary posteriors; calibrating the first set of boundary posteriors and the second set of boundary posteriors to determine relative weights for the first set of boundary posteriors and a second set of boundary posteriors; and assigning the relative weights to the first set of boundary posteriors and the second set of boundary posteriors to output the combined boundary posteriors.
1. A method for processing an input window of an audio signal for speech recognition, the input window having a plurality of frames, the method comprising: extracting auditory attention features from each of the frames of the input window; extracting phone posteriors from each of the frames of the input window; generating combined boundary posteriors from a combination of the auditory attention features and the phone posteriors using machine learning by augmenting the extracted phone posteriors by feeding the phone posteriors of neighboring frames into a machine learning algorithm to generate phone posterior context information; estimating boundaries in speech contained in the audio signal from the combined boundary posteriors; and performing speech recognition using the estimated boundaries.