Student-Teacher Training with Diverse Decision Tree Ensembles

Jeremy H.M. Wong, Mark J.F. Gales


Student-teacher training allows a large teacher model or ensemble of teachers to be compressed into a single student model, for the purpose of efficient decoding. However, current approaches in automatic speech recognition assume that the state clusters, often defined by Phonetic Decision Trees (PDT), are the same across all models. This limits the diversity that can be captured within the ensemble, and also the flexibility when selecting the complexity of the student model output. This paper examines an extension to student-teacher training that allows for the possibility of having different PDTs between teachers, and also for the student to have a different PDT from the teacher. The proposal is to train the student to emulate the logical context dependent state posteriors of the teacher, instead of the frame posteriors. This leads to a method of mapping frame posteriors from one PDT to another. This approach is evaluated on three speech recognition tasks: the Tok Pisin and Javanese low resource conversational telephone speech tasks from the IARPA Babel programme, and the HUB4 English broadcast news task.


 DOI: 10.21437/Interspeech.2017-145

Cite as: Wong, J.H., Gales, M.J. (2017) Student-Teacher Training with Diverse Decision Tree Ensembles. Proc. Interspeech 2017, 117-121, DOI: 10.21437/Interspeech.2017-145.


@inproceedings{Wong2017,
  author={Jeremy H.M. Wong and Mark J.F. Gales},
  title={Student-Teacher Training with Diverse Decision Tree Ensembles},
  year=2017,
  booktitle={Proc. Interspeech 2017},
  pages={117--121},
  doi={10.21437/Interspeech.2017-145},
  url={http://dx.doi.org/10.21437/Interspeech.2017-145}
}