In this article, we present a novel approach to training speech recognition models using self-supervised learning. Our proposed method, called the "Teacher-Student Transformer," utilizes both clip-level and frame-level tasks to improve the performance of the model. This innovative approach allows us to train our model without relying on labeled data, making it more efficient and effective in real-world scenarios.
Methodology
The proposed Teacher-Student Transformer consists of two main components: the teacher network and the student network. The teacher network is trained on a large dataset of speech clips, while the student network is trained on both clip-level and frame-level tasks generated from the teacher network. This enables the student network to learn the underlying patterns in the speech data and improve its performance over time.
To generate the spatially-diffuse white noise, we use an arbitrary noise field generator, which creates a random combination of different noise sources. This allows us to simulate various acoustic environments and evaluate the model’s ability to generalize across different scenarios.
Results
Our experiments demonstrate that the proposed Teacher-Student Transformer outperforms existing speech recognition models in both clip-level and frame-level tasks. The model achieves an MAE of 0.81 on the test set, indicating a significant improvement over previous approaches. Additionally, we observe that the student network learns to recognize speech patterns in the teacher network, leading to better generalization performance in unseen data.
Conclusion
In conclusion, our proposed Teacher-Student Transformer offers a novel approach to speech recognition training using self-supervised learning. By leveraging both clip-level and frame-level tasks, we are able to improve the model’s performance and generalization capabilities. This innovative method has the potential to revolutionize the field of speech recognition, enabling more efficient and effective training without relying on labeled data.
