Noise-robust automatic speech recognition (ASR) systems rely on feature and/or model compensation. Existing compensation techniques typically operate on the features or on the parameters of the acoustic models themselves. By contrast, a number of normalization techniques have been defined in the field of speaker verification that operate on the resulting log-likelihood scores. In this paper, we provide a theoretical motivation for likelihood normalization due to the so-called hubness phenomenon and we evaluate the benefit of several normalization techniques on ASR accuracy for the 2nd CHiME Challenge task. We show that symmetric normalization (S-norm) reduces the relative error rate by 43% alone and by 10% after feature and model compensation.
Bibliographic reference. Vincent, Emmanuel / Gkiokas, Aggelos / Schnitzer, Dominik / Flexer, Arthur (2014): "An investigation of likelihood normalization for robust ASR", In INTERSPEECH-2014, 621-625.