I am looking for ANY advice anyone can give me regarding on this problem.
I'm looking to start a project on subtitles, basically, if we take a Youtube video of a breaking news story, subtitles are generated automatically without the need of physically inputting what the speaker is saying. Now, I understand the complexity of this problem, and, I am aware of the amount of training samples I would need in order to carry out such a project.
The question is this: I have had experience in using DTW and HMM in order to train and recognise simple words "Yes", "No" and gained a lot of experience from this and as part of my previous findings I found that the DTW algorithm is slow and therefore would not be efficient for this. For this project, I imagine real-time processing is involved. Would it therefore be a wise thing to implement a Hidden Markov Model for such a problem, or, would another model/algorithm be more suitable?
Here is my basic structure:
I will upload a 3 minute video to youtube, I will then extract the raw audio from this video and then extract the features (MFCC) which will then be used in order to train the model (HMM) which will /hopefully/ identify the spoken word.
Could anyone recommend any papers that could potentially help me with tackling such a problem?
Thank you for taking time to read this. If anything is not understood, please let me know.