Segmental conditional random fields (SCRFs) and connectionist temporal
classification (CTC) are two sequence labeling methods used for end-to-end
training of speech recognition models. Both models define a transcription
probability by marginalizing decisions about latent segmentation alternatives
to derive a sequence probability: the former uses a globally normalized joint
model of segment labels and durations, and the latter classifies each frame as
either an output symbol or a "continuation" of the previous label. In this
paper, we train a recognition model by optimizing an interpolation between the
SCRF and CTC losses, where the same recurrent neural network (RNN) encoder is
used for feature extraction for both outputs. We find that this multitask
objective improves recognition accuracy when decoding with either the SCRF or
CTC models. Additionally, we show that CTC can also be used to pretrain the RNN
encoder, which improves the convergence rate when learning the joint model.

本研究中，我们使用相同的递归神经网络编码器对 SCRF 和 CTC 方法进行多任务学习，优化它们的插值损失值，发现这样的学习目标可以显著提高识别准确度，并且还表明 CTC 方法可以用于预训练 RNN 编码器，这有助于提高学习联合模型的收敛速度。