Self-Supervised Models in Automatic Whispered Speech Recognition
<|reference_start|>Self-Supervised Models in Automatic Whispered Speech Recognition: 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.<|reference_end|>
@article{farhadipour2024leveraging,
    title={Leveraging Self-Supervised Models for Automatic Whispered Speech
  Recognition},
    author={Aref Farhadipour, Homa Asadi, Volker Dellwo},
    journal={arXiv preprint arXiv:2407.21211},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21211},
    primaryClass={eess.AS cs.SD}
}