  One of the fundamental functionalities for accepting a socially assistive
robot is its communication capabilities with other agents in the environment.
In the context of the ROBIN project, situational dialogue through voice
interaction with a robot was investigated. This paper presents different speech
recognition experiments with deep neural networks focusing on producing fast
(under 100ms latency from the network itself), while still reliable models.
Even though one of the key desired characteristics is low latency, the final
deep neural network model achieves state of the art results for recognizing
Romanian language, obtaining a 9.91% word error rate (WER), when combined with
a language model, thus improving over the previous results while offering at
the same time an improved runtime performance. Additionally, we explore two
modules for correcting the ASR output (hyphen and capitalization restoration
and unknown words correction), targeting the ROBIN project's goals (dialogue in
closed micro-worlds). We design a modular architecture based on APIs allowing
an integration engine (either in the robot or external) to chain together the
available modules as needed. Finally, we test the proposed design by
integrating it in the RELATE platform and making the ASR service available to
web users by either uploading a file or recording new speech.
