On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional emission probabilities for the observations at each time step, and (ii) the conditional probability distribution of the data given the underlying hidden state is independent of any other state...