Conceptual

Semi-Supervised Acoustic Model Training for Low-Resource Speech Recognition

Building an automatic speech recognition system for a minority language, where labelled audio is scarce, by improving a modular TDNN-HMM acoustic model with semi-supervised learning on unlabelled speech so that out-of-domain audio and underrepresented dialects are recognised more accurately. The same system reframes capitalisation and punctuation restoration as a sequence-to-sequence task rather than per-token classification, and feeds human-corrected transcripts back into training in a community-driven loop.