Optimizing class priors to improve the detection of social signals in audio data

To detect social signals such as laughter and filler events in an audio recording, the most straightforward way is to utilize a Hidden Markov Model — or these days a Hidden Markov Model/Deep Neural Network (HMM/DNN) hybrid. HMM/DNNs, however, perform best if the DNN outputs are scaled by dividing them by the a priori class probabilities first, before applying a dynamic or Viterbi beam search. These class a priori probability values (or priors...