  With the advent of direct models in automatic speech recognition (ASR), the
formerly prevalent frame-wise acoustic modeling based on hidden Markov models
(HMM) diversified into a number of modeling architectures like encoder-decoder
attention models, transducer models and segmental models (direct HMM). While
transducer models stay with a frame-level model definition, segmental models
are defined on the level of label segments, directly. While
(soft-)attention-based models avoid explicit alignment, transducer and
segmental approach internally do model alignment, either by segment hypotheses
or, more implicitly, by emitting so-called blank symbols. In this work, we
prove that the widely used class of RNN-Transducer models and segmental models
(direct HMM) are equivalent and therefore show equal modeling power. It is
shown that blank probabilities translate into segment length probabilities and
vice versa. In addition, we provide initial experiments investigating decoding
and beam-pruning, comparing time-synchronous and label-/segment-synchronous
search strategies and their properties using the same underlying model.
