Disfluency correction (DC) is the process of removing disfluent elements like
fillers, repetitions and corrections from spoken utterances to create readable
and interpretable text. DC is a vital post-processing step applied to Automatic
Speech Recognition (ASR) outputs, before subsequent processing by downstream
language understanding tasks. Existing DC research has primarily focused on
English due to the unavailability of large-scale open-source datasets. Towards
the goal of multilingual disfluency correction, we present a high-quality
human-annotated DC corpus covering four important Indo-European languages:
English, Hindi, German and French. We provide extensive analysis of results of
state-of-the-art DC models across all four languages obtaining F1 scores of
97.55 (English), 94.29 (Hindi), 95.89 (German) and 92.97 (French). To
demonstrate the benefits of DC on downstream tasks, we show that DC leads to
5.65 points increase in BLEU scores on average when used in conjunction with a
state-of-the-art Machine Translation (MT) system. We release code to run our
experiments along with our annotated dataset here.

消除话语中填充词、重复和修正等不流畅元素的过程是后处理自动语音识别（ASR）输出、在下游语言理解任务之前的关键步骤。我们提供了一个高质量的人工标注的多语言不流畅纠正语料库，涵盖了四种重要的印欧语言：英语、印地语、德语和法语。在四种语言的全面结果分析中，我们获得了 97.55（英语）、94.29（印地语）、95.89（德语）和 92.97（法语）的 F1 得分。为了证明不流畅纠正对下游任务的益处，我们展示了与最先进的机器翻译（MT）系统结合使用时 BLEU 得分平均提高了 5.65 个点。我们在此处发布了运行实验的代码和我们的注释数据集。

印欧语系错误修正的大规模人工标注语料库 DISCO

DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in  Indo-European Languages

Disfluencies commonly occur in conversational speech. Speech with
disfluencies can result in noisy Automatic Speech Recognition (ASR)
transcripts, which affects downstream tasks like machine translation. In this
paper, we propose an adversarially-trained sequence-tagging model for
Disfluency Correction (DC) that utilizes a small amount of labeled real
disfluent data in conjunction with a large amount of unlabeled data. We show
the benefit of our proposed technique, which crucially depends on synthetically
generated disfluent data, by evaluating it for DC in three Indian languages-
Bengali, Hindi, and Marathi (all from the Indo-Aryan family). Our technique
also performs well in removing stuttering disfluencies in ASR transcripts
introduced by speech impairments. We achieve an average 6.15 points improvement
in F1-score over competitive baselines across all three languages mentioned. To
the best of our knowledge, we are the first to utilize adversarial training for
DC and use it to correct stuttering disfluencies in English, establishing a new
benchmark for this task.

该论文提出了一种采用对抗式训练的序列标注模型进行不流畅语音纠正的方法，结合少量有标记的真实不流畅数据和大量的未标记数据。该技术在印度语言和英语中的不流畅语音及发音障碍引起的口吃方面均表现出色，并在 F1 得分上比竞争基线平均提高了 6.15 个百分点。这是首次利用对抗性训练进行不流畅语音更正并在英语中纠正口吃等问题，为该任务建立了新的基准。

低资源语调修正的对抗训练

Adversarial Training For Low-Resource Disfluency Correction

Conversational speech often consists of deviations from the speech plan,
producing disfluent utterances that affect downstream NLP tasks. Removing these
disfluencies is necessary to create fluent and coherent speech. This paper
presents DisfluencyFixer, a tool that performs speech-to-speech disfluency
correction in English and Hindi using a pipeline of Automatic Speech
Recognition (ASR), Disfluency Correction (DC) and Text-To-Speech (TTS) models.
Our proposed system removes disfluencies from input speech and returns fluent
speech as output along with its transcript, disfluency type and total
disfluency count in source utterance, providing a one-stop destination for
language learners to improve the fluency of their speech. We evaluate the
performance of our tool subjectively and receive scores of 4.26, 4.29 and 4.42
out of 5 in ASR performance, DC performance and ease-of-use of the system. Our
tool can be accessed openly at the following link.

本研究介绍了 DisfluencyFixer，一种使用 ASR，DC 和 TTS 模型的语音转语音消除不流畅话语的工具，可提高语言学习者口语流畅度，为其提供转录、非流畅性类型和源话语中总不流畅计数的输出。