Segmental models are an alternative to frame-based models for sequence
prediction, where hypothesized path weights are based on entire segment scores
rather than a single frame at a time. Neural segmental models are segmental
models that use neural network-based weight functions. Neural segmental models
have achieved competitive results for speech recognition, and their end-to-end
training has been explored in several studies. In this work, we review neural
segmental models, which can be viewed as consisting of a neural network-based
acoustic encoder and a finite-state transducer decoder. We study end-to-end
segmental models with different weight functions, including ones based on
frame-level neural classifiers and on segmental recurrent neural networks. We
study how reducing the search space size impacts performance under different
weight functions. We also compare several loss functions for end-to-end
training. Finally, we explore training approaches, including multi-stage vs.
end-to-end training and multitask training that combines segmental and
frame-level losses
