  Negative transfer in training of acoustic models for automatic speech
recognition has been reported in several contexts such as domain change or
speaker characteristics. This paper proposes a novel technique to overcome
negative transfer by efficient selection of speech data for acoustic model
training. Here data is chosen on relevance for a specific target. A submodular
function based on likelihood ratios is used to determine how acoustically
similar each training utterance is to a target test set. The approach is
evaluated on a wide-domain data set, covering speech from radio and TV
broadcasts, telephone conversations, meetings, lectures and read speech.
Experiments demonstrate that the proposed technique both finds relevant data
and limits negative transfer. Results on a 6--hour test set show a relative
improvement of 4% with data selection over using all data in PLP based models,
and 2% with DNN features.
