We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modeled and learned explicitly. Most contemporary sequence-to-sequence models allow for from-scratch training by summing over all possible label segmentations in a given topology. In our approach there are explicit, learnable probabilities for transitions between segments as opposed to a blank label that implicitly encodes duration statistics. We implement a GPU-based forward-backward algorithm that enables the simultaneous training of label and transition probabilities. We investigate recognition results and additionally Viterbi alignments of our models. We find that while the transition model training does not improve recognition performance, it has a positive impact on the alignment quality. The generated alignments are shown to be viable targets in state-of-the-art Viterbi trainings.

利用隐马尔可夫模型（HMM）对端到端神经网络训练进行新颖的建模方法进行研究，该方法中隐藏状态之间的转移概率被显式建模和学习，通过GPU加速的前向后向算法实现标签和转移概率的同时训练，结果显示转移模型训练并不能提高识别性能，但对齐质量有积极影响，生成的对齐可用于最先进的维特比训练。

带有标签和转移概率的神经HMM的端到端训练