Automatic Speech Recognition (ASR) for adults' speeches has made significant
progress by employing deep neural network (DNN) models recently, but
improvement in children's speech is still unsatisfactory due to children's
speech's distinct characteristics. DNN models pre-trained on adult data often
struggle in generalizing children's speeches with fine tuning because of the
lack of high-quality aligned children's speeches. When generating datasets,
human annotations are not scalable, and existing forced-alignment tools are not
usable as they make impractical assumptions about the quality of the input
transcriptions. To address these challenges, we propose a new forced-alignment
tool, FASA, as a flexible and automatic speech aligner to extract high-quality
aligned children's speech data from many of the existing noisy children's
speech data. We demonstrate its usage on the CHILDES dataset and show that FASA
can improve data quality by 13.6$\times$ over human annotations.

通过提出一种新的强制对齐工具 FASA，可以从现有的嘈杂的儿童语音数据中提取高质量的对齐儿童语音数据，并显示 FASA 可以比人工标注提高数据质量 13.6 倍。

FASA：一种用于提取高质量对齐儿童语音数据的灵活自动对齐器

FASA: a Flexible and Automatic Speech Aligner for Extracting  High-quality Aligned Children Speech Data

Recent advancements in Automatic Speech Recognition (ASR) systems,
exemplified by Whisper, have demonstrated the potential of these systems to
approach human-level performance given sufficient data. However, this progress
doesn't readily extend to ASR for children due to the limited availability of
suitable child-specific databases and the distinct characteristics of
children's speech. A recent study investigated leveraging the My Science Tutor
(MyST) children's speech corpus to enhance Whisper's performance in recognizing
children's speech. This paper builds on these findings by enhancing the utility
of the MyST dataset through more efficient data preprocessing. We also
highlight important challenges towards improving children's ASR performance.
The results showcase the viable and efficient integration of Whisper for
effective children's speech recognition.

近期在自动语音识别系统（ASR）方面的最新进展，如 Whisper，已经显示出这些系统在充足的数据支持下能够接近人类水平的性能。然而，由于适合儿童的特定数据库的有限性以及儿童语音的独特特征，这一进展并不容易延伸到儿童 ASR 领域。最近的一项研究调查了利用 My Science Tutor (MyST) 儿童语音语料库提高 Whisper 在儿童语音识别方面的性能。本文在此基础上通过更高效的数据预处理提升了 MyST 数据集的实用性，同时还强调了改善儿童 ASR 性能的重要挑战。结果展示了将 Whisper 有效集成到改善儿童语音识别领域的可行性和高效性。

儿童与成人自动语音识别性能差距之桥梁

Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech  Recognition for Children VS. Adults

Automatic Speech Recognition (ASR) systems are known to exhibit difficulties
when transcribing children's speech. This can mainly be attributed to the
absence of large children's speech corpora to train robust ASR models and the
resulting domain mismatch when decoding children's speech with systems trained
on adult data. In this paper, we propose multiple enhancements to alleviate
these issues. First, we propose a data augmentation technique based on the
source-filter model of speech to close the domain gap between adult and
children's speech. This enables us to leverage the data availability of adult
speech corpora by making these samples perceptually similar to children's
speech. Second, using this augmentation strategy, we apply transfer learning on
a Transformer model pre-trained on adult data. This model follows the recently
introduced XLS-R architecture, a wav2vec 2.0 model pre-trained on several
cross-lingual adult speech corpora to learn general and robust acoustic
frame-level representations. Adopting this model for the ASR task using adult
data augmented with the proposed source-filter warping strategy and a limited
amount of in-domain children's speech significantly outperforms previous
state-of-the-art results on the PF-STAR British English Children's Speech
corpus with a 4.86% WER on the official test set.

本文提出数据增广技术和转移学习策略，使得基于 XLS-R 架构的 Transformer 模型能更好地识别儿童英语语音，测试集词错误率为 4.86%。