I was making a simple phoneme classification model for a 10 week-long class project and I ran into a small question. Is it possible, to create a model that takes a 1-second (the longest phoneme is 0.2 second but the large image is kept for context) spectrogram as input? Some people suggest creating an RNN for phoneme classification but can you build a pure CNN phoneme classification model? Any responses would be great.
Yes you can, a few years ago I made a simple CNN for a single Arabic phoneme classification. You can use spectogram or using MFCC / MFSC as features, as long all data has the same size (use padding or cropping if needed).
You may need RNN if you want to combine some phonemes to recognize a single word or longer.