  This study investigates discriminative patterns learned by neural networks
for accurate speech classification, with a specific focus on vowel
classification tasks. By examining the activations and features of neural
networks for vowel classification, we gain insights into what the networks
"see" in spectrograms. Through the use of class activation mapping, we identify
the frequencies that contribute to vowel classification and compare these
findings with linguistic knowledge. Experiments on a American English dataset
of vowels showcases the explainability of neural networks and provides valuable
insights into the causes of misclassifications and their characteristics when
differentiating them from unvoiced speech. This study not only enhances our
understanding of the underlying acoustic cues in vowel classification but also
offers opportunities for improving speech recognition by bridging the gap
between abstract representations in neural networks and established linguistic
knowledge
