Adaptive Multi-Corpora Language Model Training for Speech Recognition. (arXiv:2211.05121v1 [eess.AS])

Neural network language model (NNLM) plays an essential role in automatic
speech recognition (ASR) systems, especially in adaptation tasks when text-only
data is available. In practice, an NNLM is typically trained on a combination
of data sampled from multiple corpora. Thus, the data sampling strategy is
important to the adaptation performance. Most existing works focus on designing
static sampling strategies. However, each corpus may show varying impacts at
different NNLM training stages. In this paper, we introduce a novel adaptive
multi-corpora training algorithm that dynamically learns and adjusts the
sampling probability of each corpus along the training process. The algorithm
is robust to corpora sizes and domain relevance. Compared with static sampling
strategy baselines, the proposed approach yields remarkable improvement by
achieving up to relative 7% and 9% word error rate (WER) reductions on
in-domain and out-of-domain adaptation tasks, respectively.