  Convolutional frontends are a typical choice for Transformer-based automatic
speech recognition to preprocess the spectrogram, reduce its sequence length,
and combine local information in time and frequency similarly. However, the
width and height of an audio spectrogram denote different information, e.g.,
due to reverberation as well as the articulatory system, the time axis has a
clear left-to-right dependency. On the contrary, vowels and consonants
demonstrate very different patterns and occupy almost disjoint frequency
ranges. Therefore, we hypothesize, global attention over frequencies is
beneficial over local convolution. We obtain 2.4 % relative word error rate
reduction (rWERR) on a production scale Conformer transducer replacing its
convolutional neural network frontend by the proposed F-Attention module on
Alexa traffic. To demonstrate generalizability, we validate this on public
LibriSpeech data with a long short term memory-based listen attend and spell
architecture obtaining 4.6 % rWERR and demonstrate robustness to (simulated)
noisy conditions.
