  Automatic speech recognition (ASR) allows a natural and intuitive interface
for robotic educational applications for children. However there are a number
of challenges to overcome to allow such an interface to operate robustly in
realistic settings, including the intrinsic difficulties of recognising child
speech and high levels of background noise often present in classrooms. As part
of the EU EASEL project we have provided several contributions to address these
challenges, implementing our own ASR module for use in robotics applications.
We used the latest deep neural network algorithms which provide a leap in
performance over the traditional GMM approach, and apply data augmentation
methods to improve robustness to noise and speaker variation. We provide a
close integration between the ASR module and the rest of the dialogue system,
allowing the ASR to receive in real-time the language models relevant to the
current section of the dialogue, greatly improving the accuracy. We integrated
our ASR module into an interactive, multimodal system using a small humanoid
robot to help children learn about exercise and energy. The system was
installed at a public museum event as part of a research study where 320
children (aged 3 to 14) interacted with the robot, with our ASR achieving 90%
accuracy for fluent and near-fluent speech.
