  Uyghur is a minority language, and its resources for Automatic Speech
Recognition (ASR) research are always insufficient. THUYG-20 is currently the
only open-sourced dataset of Uyghur speeches. State-of-the-art results of its
clean and noiseless speech test task haven't been updated since the first
release, which shows a big gap in the development of ASR between mainstream
languages and Uyghur. In this paper, we try to bridge the gap by ultimately
optimizing the ASR systems, and by developing a morpheme-based decoder,
MLDG-Decoder (Morpheme Lattice Dynamically Generating Decoder for Uyghur
DNN-HMM systems), which has long been missing. We have open-sourced the
decoder. The MLDG-Decoder employs an algorithm, named as "on-the-fly
composition with FEBABOS", to allow the back-off states and transitions to play
the role of a relay station in on-the-fly composition. The algorithm empowers
the dynamically generated graph to constrain the morpheme sequences in the
lattices as effectively as the static and fully composed graph does when a
4-Gram morpheme-based Language Model (LM) is used. We have trained deeper and
wider neural network acoustic models, and experimented with three kinds of
decoding schemes. The experimental results show that the decoding based on the
static and fully composed graph reduces state-of-the-art Word Error Rate (WER)
on the clean and noiseless speech test task in THUYG-20 to 14.24%. The
MLDG-Decoder reduces the WER to 14.54% while keeping the memory consumption
reasonable. Based on the open-sourced MLDG-Decoder, readers can easily
reproduce the experimental results in this paper.
