Unsupervised acoustic model training for the Korean language

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… (More)