  Acoustic modeling of raw waveform and learning feature extractors as part of
the neural network classifier has been the goal of many studies in the area of
automatic speech recognition (ASR). Recently, one line of research has focused
on frameworks that can be pre-trained on audio-only data in an unsupervised
fashion and aim at improving downstream ASR tasks. In this work, we investigate
the usefulness of one of these front-end frameworks, namely wav2vec, for hybrid
ASR systems. In addition to deploying a pre-trained feature extractor, we
explore how to make use of an existing acoustic model (AM) trained on the same
task with different features as well. Another neural front-end which is only
trained together with the supervised ASR loss as well as traditional Gammatone
features are applied for comparison. Moreover, it is shown that the AM can be
retrofitted with i-vectors for speaker adaptation. Finally, the described
features are combined in order to further advance the performance. With the
final best system, we obtain a relative improvement of 4% and 6% over our
previous best model on the LibriSpeech test-clean and test-other sets.
