  Online speech recognition is crucial for developing natural human-machine
interfaces. This modality, however, is significantly more challenging than
off-line ASR, since real-time/low-latency constraints inevitably hinder the use
of future information, that is known to be very helpful to perform robust
predictions. A popular solution to mitigate this issue consists of feeding
neural acoustic models with context windows that gather some future frames.
This introduces a latency which depends on the number of employed look-ahead
features. This paper explores a different approach, based on estimating the
future rather than waiting for it. Our technique encourages the hidden
representations of a unidirectional recurrent network to embed some useful
information about the future. Inspired by a recently proposed technique called
Twin Networks, we add a regularization term that forces forward hidden states
to be as close as possible to cotemporal backward ones, computed by a "twin"
neural network running backwards in time. The experiments, conducted on a
number of datasets, recurrent architectures, input features, and acoustic
conditions, have shown the effectiveness of this approach. One important
advantage is that our method does not introduce any additional computation at
test time if compared to standard unidirectional recurrent networks.
