Distributed speech processing in miPad's multimodal user interface

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Deep Learning: Methods and Applications (2014, background): “For example, a prototype called MiPad for multi-modal interactions involving capturing, leaning, coordinating, and rendering a mix of speech, touch, and visual information was developed and reported in [175, 103].”
Challenges in adopting speech recognition (2004, background): “Nevertheless, achieving workable solutions or even solutions close to human-level speech recognition in certain highly constrained tasks is on the horizon with carefully designed, limited-domain applications taking advantage of multimodal interactions (for example, [5, 10]).”
An Overview of Noise-Robust Automatic Speech Recognition (2014, background): “For such large-scale, real-world applications, noise robustness is becoming an increasingly important core technology since ASR needs to work in much more dif ﬁ cult acoustic environments than in the past [4].”
Speech-Centric Information Processing: An Optimization-Oriented Approach (2013, background): “TC-STAR (http://www.tc-star.org) in Europe and GALE (http://www. darpa.mil/ipto/programs/gale) in the United States are the most prominent SLT research projects.”