ASR models are commonly trained with the cross-entropy criterion to increase
the probability of a target token sequence. While optimizing the probability of
all tokens in the target sequence is sensible, one may want to de-emphasize
tokens that reflect transcription errors. In this work, we propose a novel
token-weighted RNN-T criterion that augments the RNN-T objective with
token-specific weights. The new objective is used for mitigating accuracy loss
from transcriptions errors in the training data, which naturally appear in two
settings: pseudo-labeling and human annotation errors. Experiments results show
that using our method for semi-supervised learning with pseudo-labels leads to
a consistent accuracy improvement, up to 38% relative. We also analyze the
accuracy degradation resulting from different levels of WER in the reference
transcription, and show that token-weighted RNN-T is suitable for overcoming
this degradation, recovering 64%-99% of the accuracy loss.

使用基于标记权重的 RNN-T 准则来缓解音频识别中的转录错误，并且在半监督学习中使用该方法可以显著提高准确性。

基于令牌加权的 RNN-T 模型用于学习有缺陷的数据

Token-Weighted RNN-T for Learning from Flawed Data

The performance of Automatic Speech Recognition (ASR) systems has constantly
increased in state-of-the-art development. However, performance tends to
decrease considerably in more challenging conditions (e.g., background noise,
multiple speaker social conversations) and with more atypical speakers (e.g.,
children, non-native speakers or people with speech disorders), which signifies
that general improvements do not necessarily transfer to applications that rely
on ASR, e.g., educational software for younger students or language learners.
In this study, we focus on the gap in performance between recognition results
for native and non-native, read and spontaneous, Swedish utterances transcribed
by different ASR services. We compare the recognition results using Word Error
Rate and analyze the linguistic factors that may generate the observed
transcription errors.

使用词错误率比较母语和非母语、朗读和即兴的瑞典话语在不同的 ASR 服务中的识别结果，并分析可能导致观察到的转录错误的语言因素。

L1 和 L2 瑞典语说话者的 ASR 结果比较分析

You don't understand me!: Comparing ASR results for L1 and L2 speakers  of Swedish

High-quality human transcription is essential for training and improving
Automatic Speech Recognition (ASR) models. Recent study~\cite{libricrowd} has
found that every 1% worse transcription Word Error Rate (WER) increases
approximately 2% ASR WER by using the transcriptions to train ASR models.
Transcription errors are inevitable for even highly-trained annotators.
However, few studies have explored human transcription correction. Error
correction methods for other problems, such as ASR error correction and
grammatical error correction, do not perform sufficiently for this problem.
Therefore, we propose HTEC for Human Transcription Error Correction. HTEC
consists of two stages: Trans-Checker, an error detection model that predicts
and masks erroneous words, and Trans-Filler, a sequence-to-sequence generative
model that fills masked positions. We propose a holistic list of correction
operations, including four novel operations handling deletion errors. We
further propose a variant of embeddings that incorporates phoneme information
into the input of the transformer. HTEC outperforms other methods by a large
margin and surpasses human annotators by 2.2% to 4.5% in WER. Finally, we
deployed HTEC to assist human annotators and showed HTEC is particularly
effective as a co-pilot, which improves transcription quality by 15.1% without
sacrificing transcription velocity.

我们提出了一种用于人类转录错误纠正的人工检错和填充模型（HTEC），其包含两个阶段：Trans-Checker 用于检测和屏蔽错误的单词，Trans-Filler 用于填补屏蔽位置，该模型在转录质量方面表现出较大优势，并提高了 15.1% 的转录质量而不影响转录速度。

HTEC: 人工听写错误校正

HTEC: Human Transcription Error Correction

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。