Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR

arXiv:2403.10937v1 Announce Type: cross
Abstract: This paper addresses the problem of improving speech recognition accuracy with lattice rescoring in low-resource languages where the baseline language model is insufficient for generating inclusive lattices. We minimally augment the baseline language model with word unigram counts that are present in a larger text corpus of the target language but absent in the baseline. The lattices generated after decoding with such an augmented baseline language model are more comprehensive. We obtain 21.8% (Telugu) and …

abstract accuracy arxiv asr cs.cl cs.lg decoding eess.as language language model languages lattice low paper recognition speech speech recognition type word