Unsupervised acoustic model training for the Korean language
Antoine Laurent, William M. Hartmann, Lori F Lamel · 2014
This paper investigates unsupervised training strategies for the Korean language in the context of the DGA RAPID Rapmat project. As with previous studies, we begin with only a small amount of manually transcribed data to build preliminary acoustic models. Using the initial models, a larger set of untranscribed audio data is decoded to produce approximate transcripts. We compare both GMM and DNN acoustic models for both the unsupervised transcription and the final recognition system. While the DNN acoustic models produce a lower word error rate on the test set, training on the transcripts from the GMM system provides the best overall performance. We also achieve better performance by expanding the original phone set. Finally, we examine the efficacy of automatically building a test set by comparing system performance both before and after manually correcting the test set.