# How to use frame based speech features for learning using a neural network classifier?

I am doing supervised learning on speech audio files using neural networks. For this purpose, I'll have to extract features from the audio file. But since an audio file is a time varying signal, it is generally divided into multiple frames and then features like MFCC etc are extracted from each frame. So how should I encode my feature vector for each training example(audio file) considering that it will be divided into different no of frames(depending on duration of file)?

• Sorry, I can't understand what you're asking. What research have you done? Do you understand how the MFCC features are typically computed? If you do, then what's the question? There's no "encoding" to be done; the MFCC features for each frame are already a 13-vector of real numbers, so if you have $n$ frames, you get $13n$ real numbers. Please edit your question to explain what research you've done, what you understand, what specifically you are confused about, and generally flesh it out so we can understand what you are confused about. – D.W. Apr 30 '15 at 6:50
• Thanks for your response D.W. I am a beginner in the field of ML and audio signal processing, so some of my understanding may be incorrect. What i meant to ask is that for each audio file I have 13n[i] features where n[i] is no of frames in the ith audio file. But to feed these to a neutral network, the number of features must be same for each audio file. But here they vary as per the no. of frames. So how should I take care of it? – ksb Apr 30 '15 at 12:00
• OK, that makes more sense. Please edit your question accordingly. Comments exist only to help you improve your question; all clarifications belong in the question, and shouldn't be left in only the comments. Also, I suggest you tell what what you want to use supervised learning for. In other words, what is the classification task? What property are you trying to learn/estimate? This will affect the best solution to this problem. – D.W. Apr 30 '15 at 19:55