INTERSPEECH 2013
14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

iVector-Based Acoustic Data Selection

Olivier Siohan, Michiel Bacchiani

Google, USA

This paper presents a data selection approach where spoken utterances 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, a low dimensional vector indicating the coordinate 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 domains. Last, we present experimental speech recognition results based on a system trained on a data set constructed by the proposed algorithm and a comparison with random data selection.

Full Paper

Bibliographic reference.  Siohan, Olivier / Bacchiani, Michiel (2013): "ivector-based acoustic data selection", In INTERSPEECH-2013, 657-661.