Helicopter cockpit speech recognition method based on transfer learning and context biasing

Currently, Chinese speech recognition technology is generally designed for common domains, primarily focusing on accurate recognition of standard Mandarin Chinese in low-noise environments. However, helicopter cockpit speech presents unique challenges, characterized by high-noise environments, specific industry jargon, low contextual relevance, and a lack of publicly available datasets. To address these issues, this paper proposes a helicopter cockpit speech recognition method based on transfer learning and context biasing. By fine-tuning a general speech recognition model, we aim to better adapt it to the characteristics of speech in helicopter cockpits. This study explores noise reduction processing, context biasing, and speed perturbation in helicopter cockpit speech data. Combining pre-trained models with language models, we conduct transfer training to develop a specialized model for helicopter cockpit speech recognition. Finally, the effectiveness of this method is validated using a real dataset. Experimental results show that, on the helicopter speech dataset, this method reduces the word error rate from 72.69% to 12.58%. Furthermore, this approach provides an effective solution for small-sample speech recognition, enhancing model performance on limited datasets.