Adapting DeCRED for Russian Language and Disordered Speech
Hello DeCRED authors and community members! 👋
I am currently working on my diploma project, which focuses on automatic speech recognition (ASR) for disordered speech in Russian. My approach is based on an encoder-decoder model (LSTM + Attention), and I am training it on a specialized dataset containing speech samples from individuals with dysarthria and other speech impairments.
I see DeCRED as a reference model and would like to explore how I can adapt a similar architecture for Russian speech recognition. However, I have a few key questions:
1️⃣ How does DeCRED handle acoustic variations in speech? What preprocessing modifications might help adapt the model for atypical speech patterns?
2️⃣ Can DeCRED be modified to work with Russian? What key parameters would need to be adjusted in the model’s architecture?
3️⃣ How does DeCRED handle noise and mispronunciations? Are there built-in mechanisms to improve robustness against unclear speech?
4️⃣ Are there any recommendations for improving ASR models for low-resource languages or specialized user groups?
5️⃣ Which tools/approaches would you suggest for fine-tuning the model effectively?
Current tech stack I’m using:
TensorFlow / PyTorch / SpeechBrain for training.
Librosa / FFmpeg / OpenCV for data preprocessing.
Gradio for developing the user interface.
Kaggle as a source for additional training data.
I would really appreciate any insights, suggestions, or experiences from those who have worked on adapting DeCRED for different languages or non-standard speech recognition.
Thank you for your time and help
Hello Ahmed,
I have to say, I am personally very intrigued by questions 1-4. How about you conduct some experiments and tell us? :)) Some of those might even make nice papers..
Best regards,
Simon