DySANA: dynamic speech and noise adaptation for voice activity detection

We describe a method of simultaneusly tracking noise and speech levels for signal-to-noise ratio adaptive speech endpoint detection. The method is based on the Kalman filter framework with switching observations and uses a dynamic distribution that 1) limits the rate of change of these levels 2) enforces a range on the values for the two levels and 3) enforces a ratio between the noise and the signal levels. We call this a Lombard dynamic distribution since it encodes the expectation that a speaker will increase his or her vocal intensity in noise. The method also employs a state transition matrix which encodes a prior on the states and provides a continuity constraint. The new method provides 46.1% relative improvement in WER over a baseline GMM based endpointer at 20 dB SNR.