  In automatic speech recognition, any factor that alters the acoustic
properties of speech can pose a challenge to the system's performance. This
paper presents a novel approach for automatic whispered speech recognition in
the Irish dialect using the self-supervised WavLM model. Conventional automatic
speech recognition systems often fail to accurately recognise whispered speech
due to its distinct acoustic properties and the scarcity of relevant training
data. To address this challenge, we utilized a pre-trained WavLM model,
fine-tuned with a combination of whispered and normal speech data from the
wTIMIT and CHAINS datasets, which include the English language in Singaporean
and Irish dialects, respectively. Our baseline evaluation with the OpenAI
Whisper model highlighted its limitations, achieving a Word Error Rate (WER) of
18.8% on whispered speech. In contrast, the proposed WavLM-based system
significantly improved performance, achieving a WER of 9.22%. These results
demonstrate the efficacy of our approach in recognising whispered speech and
underscore the importance of tailored acoustic modeling for robust automatic
speech recognition systems. This study provides valuable insights into
developing effective automatic speech recognition solutions for challenging
speech affected by whisper and dialect. The source codes for this paper are
freely available.
