In recent years, the natural language processing community has moved away
from task-specific feature engineering, i.e., researchers discovering ad-hoc
feature representations for various tasks, in favor of general-purpose methods
that learn the input representation by themselves. However, state-of-the-art
approaches to disfluency detection in spontaneous speech transcripts currently
still depend on an array of hand-crafted features, and other representations
derived from the output of pre-existing systems such as language models or
dependency parsers. As an alternative, this paper proposes a simple yet
effective model for automatic disfluency detection, called an
auto-correlational neural network (ACNN). The model uses a convolutional neural
network (CNN) and augments it with a new auto-correlation operator at the
lowest layer that can capture the kinds of "rough copy" dependencies that are
characteristic of repair disfluencies in speech. In experiments, the ACNN model
outperforms the baseline CNN on a disfluency detection task with a 5% increase
in f-score, which is close to the previous best result on this task.

本研究提出了一种名为自相关神经网络 (ACNN) 的自动语音紊乱检测模型，其使用卷积神经网络 (CNN) 结合自相关操作，从而能够更好地捕捉修复性语音的相关依赖关系，实验表明，ACNN 模型在语音紊乱检测任务中的性能要优于传统的 CNN 模型，能够提高 5% 的 f-score。