Don't multiply lightly: Quantifying problems with the acoustic model assumptions in speech recognition

Abstract

We describe a series of experiments simulating data from the standard Hidden Markov Model (HMM) framework used for speech recognition. Starting with a set of test transcriptions, we begin by simulating every step of the generative process. In each subsequent experiment, we substitute a real component for a simulated component (real state durations rather than simulating from the transition models, for example), and compare the word error rates of the resulting data, thus quantifying the relative costs of each modeling assumption. A novel sampling process allows us to test the independence assumptions of the HMM, which appear to present far more serious problems than the other data/model mismatches.