ivector-based acoustic data selection
Olivier Siohan, Michiel Bacchiani · 2013
This paper presents a data selection approach where spoken ut-terances are selected in a sequential fashion from a large out-of-domain data set to match the utterance distribution of an in-domain data set. We propose to represent each utterance by its iVector [1], a low dimensional vector indicating the coordi-nate of that utterance in a subspace acoustic model. We show that the distribution of iVectors can characterize a data set and enables distinguishing subsets of utterances from different do-mains. Last, we present experimental speech recognition results based on a system trained on a data set constructed by the pro-posed algorithm and a comparison with random data selection. Index Terms: speech recognition, data selection, acoustic mod-eling