  High-performance hybrid automatic speech recognition (ASR) systems are often
trained with clustered triphone outputs, and thus require a complex training
pipeline to generate the clustering. The same complex pipeline is often
utilized in order to generate an alignment for use in frame-wise cross-entropy
training. In this work, we propose a flat-start factored hybrid model trained
by modeling the full set of triphone states explicitly without relying on
clustering methods. This greatly simplifies the training of new models.
Furthermore, we study the effect of different alignments used for Viterbi
training. Our proposed models achieve competitive performance on the
Switchboard task compared to systems using clustered triphones and other
flat-start models in the literature.
