I have been researching about Speech Recognition and I have decided to take the MFCC approach to solving this problem of detecting whether someone is saying either "Yes" or "No". So as mentioned before (My steps so far):
- Read in Audio File
- Split the Audio Signal into blocks (600 samples, 30msec long)
- Strip the blocks that do not warrant consideration (Total Energy / Zero-crossing)
So I am going to construct the MFCC based on this paper (http://arxiv.org/pdf/1003.4083.pdf) and it has the following steps:
3) Hamming Windowing
5) Mel Filter Bank Processing
6) Discrete Cosine Transform
7) Delta Energy and Delta Spectrum
This makes sense to me (Kind of) and I am going to research into each of these steps. BUT should I perform the MFCC on the resulting blocks that I have already done with (steps 1, 2, 3) at the top of this question, or, should I not carry out these steps and just start from the beginning and compute the MFCC and will I still be able to implement a Hidden Markov Model?
The other question is, if I split the signal into "Frames" (2D vector) will the resulting MFCC be a 2D vector, or a 1D vector?
Hope someone can help :)!