Speech disfluencies, such as filled pauses or repetitions, are disruptions in
the typical flow of speech. Stuttering is a speech disorder characterized by a
high rate of disfluencies, but all individuals speak with some disfluencies and
the rates of disfluencies may by increased by factors such as cognitive load.
Clinically, automatic disfluency detection may help in treatment planning for
individuals who stutter. Outside of the clinic, automatic disfluency detection
may serve as a pre-processing step to improve natural language understanding in
downstream applications. With this wide range of applications in mind, we
investigate language, acoustic, and multimodal methods for frame-level
automatic disfluency detection and categorization. Each of these methods relies
on audio as an input. First, we evaluate several automatic speech recognition
(ASR) systems in terms of their ability to transcribe disfluencies, measured
using disfluency error rates. We then use these ASR transcripts as input to a
language-based disfluency detection model. We find that disfluency detection
performance is largely limited by the quality of transcripts and alignments. We
find that an acoustic-based approach that does not require transcription as an
intermediate step outperforms the ASR language approach. Finally, we present
multimodal architectures which we find improve disfluency detection performance
over the unimodal approaches. Ultimately, this work introduces novel approaches
for automatic frame-level disfluency and categorization. In the long term, this
will help researchers incorporate automatic disfluency detection into a range
of applications.

说话不流畅现象，如填充式停顿或重复，是典型言语流畅的干扰。本文研究语言、声学和多模态方法在自动裁剪检测和分类中的应用，发现基于声学的方法在性能上超过语言方法，同时引入多模态结构提升了检测性能。这些创新方法有助于将自动裁剪检测应用于各种应用场景。

自动识别未转录语音中的干扰

Automatic Disfluency Detection from Untranscribed Speech

This paper explores contexts associated with errors in transcrip-tion of
spontaneous speech, shedding light on human perceptionof disfluencies and other
conversational speech phenomena. Anew version of the Switchboard corpus is
provided with disfluency annotations for careful speech transcripts, together
with results showing the impact of transcription errors on evaluation of
automatic disfluency detection.

本研究探讨了与口语转录中出现错误相关的语境，揭示了人类感知语言中的流利性和其他对话语言现象。提供一个新版本的 Switchboard 语料库，用于谨慎地转录口语，并展示了转录错误对自动检测口语中语重心长现象的评估的影响。