GPT-2 is a language model that's great at classification, and CLIP is a neural network trained on lots of text and images. T5 is also a language model used for natural language processing tasks, and BERT is another language model that's pretty good at zero-shot classification tasks.

Lastly, there's ViT, mainly used for image classification but has also been shown to work for speech classification. So, if you want to use these models for zero-shot speech classification, fine-tune them on some labeled data, and you're good to go!

Ryan Torres : not sure how speech can be fed directly to NLP models like T5 or BERT unless we use Speech to Text (STT). If we use STT, there might be noise in speech to text translation itself.

Zero-shot speech classification is the task of classifying audio data into categories without requiring training data for each category. Deep learning models have shown promising results for zero-shot speech classification. Here are some of the latest deep learning models for zero-shot speech classification:

It's important to note that the field of zero-shot speech classification is rapidly evolving, and new models are constantly being developed. Therefore, it's essential to stay up-to-date with the latest research and evaluate the performance of different models on specific tasks.

Thanks SHAHEEN MOHAMMED ALHIRMIZY