  Phoneme recognition is a very important part of speech recognition that
requires the ability to extract phonetic features from multiple frames. In this
paper, we compare and analyze CNN, RNN, Transformer, and Conformer models using
phoneme recognition. For CNN, the ContextNet model is used for the experiments.
First, we compare the accuracy of various architectures under different
constraints, such as the receptive field length, parameter size, and layer
depth. Second, we interpret the performance difference of these models,
especially when the observable sequence length varies. Our analyses show that
Transformer and Conformer models benefit from the long-range accessibility of
self-attention through input frames.
