This paper investigates the combination of discriminative adaptation techniques. The discriminative MAP adaptation (DMAP) and discriminative feature MLLR (DfMLLR) are examined. Since each of the methods is proposed for distinct amount of adaptation data it is useful to combine them in order to preserve the systems performance in situations with varying amount of adaptation data. Generally, DfMLLR and DMAP are executed subsequently (DMAP preceded by DfMLLR) demanding to approach the data twice. Since both methods address the data through the same statistics an one-pass-combination was proposed in order to decrease the time consumption. The one-pass-combination utilizes the advantage of DfMLLR method to transform directly the feature vectors. However, instead of feature vectors the statistics are transformed, what allows to use already computed statistics without the need to process the data once again. All approaches are compared also to their non-discriminative alternatives.
Bibliographic reference. Machlica, Lukáš / Zajíc, Zbyněk / Müller, Luděk (2010): "Discriminative adaptation based on fast combination of DMAP and dfMLLR", In INTERSPEECH-2010, 534-537.