  The ongoing research scenario for automatic speech recognition (ASR)
envisions a clear division between end-to-end approaches and classic modular
systems. Even though a high-level comparison between the two approaches in
terms of their requirements and (dis)advantages is commonly addressed, a closer
comparison under similar conditions is not readily available in the literature.
In this work, we present a comparison focused on the label topology and
training criterion. We compare two discriminative alignment models with hidden
Markov model (HMM) and connectionist temporal classification topology, and two
first-order label context ASR models utilizing factored HMM and strictly
monotonic recurrent neural network transducer, respectively. We use different
measurements for the evaluation of the alignment quality, and compare word
error rate and real time factor of our best systems. Experiments are conducted
on the LibriSpeech 960h and Switchboard 300h tasks.
