  Automatic speech recognition (ASR) systems typically use handcrafted feature
extraction pipelines. To avoid their inherent information loss and to achieve
more consistent modeling from speech to transcribed text, neural raw waveform
feature extractors (FEs) are an appealing approach. Also the wav2vec 2.0 model,
which has recently gained large popularity, uses a convolutional FE which
operates directly on the speech waveform. However, it is not yet studied
extensively in the literature. In this work, we study its capability to replace
the standard feature extraction methods in a connectionist temporal
classification (CTC) ASR model and compare it to an alternative neural FE. We
show that both are competitive with traditional FEs on the LibriSpeech
benchmark and analyze the effect of the individual components. Furthermore, we
analyze the learned filters and show that the most important information for
the ASR system is obtained by a set of bandpass filters.
