Data-selective Transfer Learning for Multi-Domain Speech Recognition

  title={Data-selective Transfer Learning for Multi-Domain Speech Recognition},
  author={Mortaza Doulaty and Oscar Saz-Torralba and Thomas Hain},
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… CONTINUE READING
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