Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based
  Augmentation
Massa Baali Ibrahim Almakky Shady Shehata Fakhri Karray
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.