  In this work, we compare from-scratch sequence-level cross-entropy (full-sum)
training of Hidden Markov Model (HMM) and Connectionist Temporal Classification
(CTC) topologies for automatic speech recognition (ASR). Besides accuracy, we
further analyze their capability for generating high-quality time alignment
between the speech signal and the transcription, which can be crucial for many
subsequent applications. Moreover, we propose several methods to improve
convergence of from-scratch full-sum training by addressing the alignment
modeling issue. Systematic comparison is conducted on both Switchboard and
LibriSpeech corpora across CTC, posterior HMM with and w/o transition
probabilities, and standard hybrid HMM. We also provide a detailed analysis of
both Viterbi forced-alignment and Baum-Welch full-sum occupation probabilities.
