Semi-Supervised Training for Lecture Transcription in Resource-Scarce Environments
Date
2014Author
De Villiers, Pieter
Barnard, Etienne
van Heerden, Charl J.
Jooste, Petri
Metadata
Show full item recordAbstract
We present a study where standard semi-supervised
training methods are applied in a resource-scarce environment
to build lecture transcription systems. Experiments are
conducted on two different corpora which one can expect to
be available in resource-scarce environments. These include 1)
speaker- and domain-specific data where a single South African
English lecturer presents the “Operating Systems” course, and
2) Afrikaans speaker-independent and domain non-specific data
collected from science and law courses. Different amounts of
acoustic and language model data are used for training the
respective models. We find that lecture transcription systems
in resource-scarce environments can benefit substantially from
semi-supervised training methods. We also describe a small, new
corpus of spoken lectures which is freely available in the public
domain.
URI
https://www.semanticscholar.org/paper/Semi-Supervised-Training-for-Lecture-Transcription-Villiers-Barnard/410b436b1193c5b394f7905e3f0ebcda06edd19ehttp://hdl.handle.net/10394/26496
Collections
- Faculty of Engineering [1123]