Improving Uyghur ASR Systems With Decoders Using Morpheme-based Language Models
Gaoce Huang, Zicheng Qiu, Baoping Zhou, Turghunjan Mamut, Wei Jiang · 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · 2022
We develop and open-source a morpheme-based decoder wrapper, Morpheme Lattice Dynamically Generating Decoder(MLDG-Decoder) for Uyghur Deep Neural Network-Hidden Markov Model(DNN-HMM) systems. The Morpheme Lattice Dynamically Generating Decoder employs an algorithm, named as “on-the-fly composition with Forbidden Epsilons Activated by Back-Off States(FEABOS),” to allow the back-off states and back-off transitions to play the role of a relay station during 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. Based on the deeper and wider neural network acoustic models and the Morpheme Lattice Dynamically Generating Decoder, we experiment with three morpheme-based decoding strategies for Deep Neural Network-Hidden Markov Model hybrid systems: using a Morpheme Lattice Dynamically Generating Decoder, the two-pass decoding based on rescoring, and the fully static and morpheme-based decoding. The results show that decoding based on a fully static decoding graph reduces the minimum Word Error Rate(WER) to 14.24%. Morpheme Lattice Dynamically Generating Decoder reduces Word Error Rate to 14.54% and maintains reasonable memory consumption.