Despite major advancements in Automatic Speech Recognition (ASR), the
state-of-the-art ASR systems struggle to deal with impaired speech even with
high-resource languages. In Arabic, this challenge gets amplified, with added
complexities in collecting data from dysarthric speakers. In this paper, we aim
to improve the performance of Arabic dysarthric automatic speech recognition
through a multi-stage augmentation approach. To this effect, we first propose a
signal-based approach to generate dysarthric Arabic speech from healthy Arabic
speech by modifying its speed and tempo. We also propose a second stage
Parallel Wave Generative (PWG) adversarial model that is trained on an English
dysarthric dataset to capture language-independant dysarthric speech patterns
and further augment the signal-adjusted speech samples. Furthermore, we propose
a fine-tuning and text-correction strategies for Arabic Conformer at different
dysarthric speech severity levels. Our fine-tuned Conformer achieved 18% Word
Error Rate (WER) and 17.2% Character Error Rate (CER) on synthetically
generated dysarthric speech from the Arabic commonvoice speech dataset. This
shows significant WER improvement of 81.8% compared to the baseline model
trained solely on healthy data. We perform further validation on real English
dysarthric speech showing a WER improvement of 124% compared to the baseline
trained only on healthy English LJSpeech dataset.

本文旨在通过多阶段增强方法来改进阿拉伯语言障碍患者的自动语音识别性能，具体包括使用基于信号的方法生成阿拉伯语言障碍患者的语音，并通过对英语语言障碍患者语音数据进行训练以进行多语言扩充，以及在不同的语音障碍程度下进行微调和文本纠正策略。通过这些方法，本文在阿拉伯语言障碍患者的语音数据集上实现了 18% 的词错误率和 17.2% 的字符错误率，相比只针对健康数据训练的基础模型，实现了 81.8% 的 WER 提升，并在真实的英文语言障碍患者语音数据集上实现了 124% 的 WER 提升。

使用对抗和基于信号增强技术的阿拉伯语运动失调言语识别

Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based  Augmentation

Automatic recognition of disordered and elderly speech remains a highly
challenging task to date due to the difficulty in collecting such data in large
quantities. This paper explores a series of approaches to integrate domain
adapted SSL pre-trained models into TDNN and Conformer ASR systems for
dysarthric and elderly speech recognition: a) input feature fusion between
standard acoustic frontends and domain adapted wav2vec2.0 speech
representations; b) frame-level joint decoding of TDNN systems separately
trained using standard acoustic features alone and with additional wav2vec2.0
features; and c) multi-pass decoding involving the TDNN/Conformer system
outputs to be rescored using domain adapted wav2vec2.0 models. In addition,
domain adapted wav2vec2.0 representations are utilized in
acoustic-to-articulatory (A2A) inversion to construct multi-modal dysarthric
and elderly speech recognition systems. Experiments conducted on the UASpeech
dysarthric and DementiaBank Pitt elderly speech corpora suggest TDNN and
Conformer ASR systems integrated domain adapted wav2vec2.0 models consistently
outperform the standalone wav2vec2.0 models by statistically significant WER
reductions of 8.22% and 3.43% absolute (26.71% and 15.88% relative) on the two
tasks respectively. The lowest published WERs of 22.56% (52.53% on very low
intelligibility, 39.09% on unseen words) and 18.17% are obtained on the
UASpeech test set of 16 dysarthric speakers, and the DementiaBank Pitt test set
respectively.

本文探讨了将领域适应 SSL 预训练模型整合到 TDNN 和 Conformer ASR 系统中，以识别老年人和患有发音障碍的人的语音，并通过实验获得了很好的效果。