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
Hello,
I wrote created a new model for this.
but this what i got so far:
The model is training correctly - The training loss is generally decreasing, which shows the model is learning from the training data.
Overfitting concern - The validation loss is higher than training loss and not improving, which suggests the model might be overfitting to the training data.
WER is worsening - The WER (Word Error Rate) has increased from 1.0 to 2.0, which seems counterintuitive. This happens because:
Initially, the model might predict nothing (empty strings) for all samples
As it learns, it starts making predictions that are partly right but contain errors
This can temporarily increase WER before it gets better
Training is slow on CPU - Each epoch takes around 20 minutes on CPU, which is expected for this type of model.
i need to fix those things but i still have no idea how this can be done correctly.
Training Summary
Model Architecture: 3.8 million parameters
Training Duration: 131 minutes (2.2 hours) on CPU
Early Stopping: Triggered after 5 epochs of no improvement in validation loss
Best Model: Saved at epoch 1 with validation loss of 3.2364
The model shows clear signs of overfitting:
Training loss consistently decreases (from 2.41 to 1.75)
Validation loss remains high or worsens (3.24 to 3.53)
WER fluctuates but remains very high (perfect would be 0.0)