Feature normalization via ANN/HMM inversion for speech recognition under noisy conditions

Spoken human-machine interaction in real-world environments requires acoustic models that are robust to changes in acoustic conditions, e.g. presence of noise. Unfortunately, the popular Hidden Markov models (HMM) are not noise tolerant. One way to increase recognition performance rely on the acquisition of a small adaptation set of noisy utterances, that is used to estimate a normalization mapping between noisy and clean features to be fed into the acoustic model. In this research we develop a maximum-likelihood gradient-ascent training algorithm (instead of the usual least squares regression) for a neural normalization module to be combined with a hybrid connectionist/HMM recognizer. The algorithm is inspired by the so-called "inversion principle". Simulation results on a real-world speaker-independent continuous speech corpus of connected Italian digits, corrupted by additive noise, validate the approach: a small neural net (13 hidden neurons) trained over a single adaptation utterance for just one iteration yields 18.79% relative word error rate (WER) reduction over the bare hybrid, and a 65.10% relative WER reduction over the Gaussian-based HMM.