一种基于神经语言模型的可量化格重评分策略
A Parallelizable Lattice Rescoring Strategy with Neural Language Models
摘要：
本文提出了一种并行计算策略和基于后验的格扩展算法，用于自动语音识别的神经语言模型（LM）的格重评分。首先，提出了基于后验的格扩展算法，对第一遍解码得到的格进行扩展。其次，每个扩展的格被转换为覆盖每个弧的假设的最小列表。每个假设被约束为它所包括的至少一个弧的最佳路径。对于每个网格，并行计算最小列表的神经LM分数，然后在重新评分阶段将其整合回网格。在Switchboard数据集上的实验表明，该策略获得了相当的识别性能，并产生更紧凑的格比竞争的基线方法。此外，并行重新评分方法通过简化PyTorch训练的神经LM与Kaldi的晶格重新评分的集成提供了更大的灵活性。
英文摘要：
This paper proposes a parallel computation strategy and a posterior-based lattice expansion algorithm for efficient lattice rescoring with neural language models (LMs) for automatic speech recognition. First, lattices from first-pass decoding are expanded by the proposed posterior-based lattice expansion algorithm. Second, each expanded lattice is converted into a minimal list of hypotheses that covers every arc. Each hypothesis is constrained to be the best path for at least one arc it includes. For each lattice, the neural LM scores of the minimal list are computed in parallel and are then integrated back to the lattice in the rescoring stage. Experiments on the Switchboard dataset show that the proposed rescoring strategy obtains comparable recognition performance and generates more compact lattices than a competitive baseline method. Furthermore, the parallel rescoring method offers more flexibility by simplifying the integration of PyTorch-trained neural LMs for lattice rescoring with Kaldi.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
