Voice is a natural mode of expression offered by modern computer-based systems. Qualitative perspectives on voice-based user experiences (voice UX) offer rich descriptions of complex interactions that numbers alone cannot fully represent. We conducted a systematic review of the literature on qualitative approaches to voice UX, capturing the nature of this body of work in a systematic map and offering a qualitative synthesis of findings. We highlight the benefits of qualitative methods for voice UX research, identify opportunities for increasing rigour in methods and outcomes, and distill patterns of experience across a diversity of devices and modes of qualitative praxis.

When we started learning COBOL in high school, one of the first things the teacher introduced was the concept of GIGO. GIGO stands for "garbage in, garbage out". If we input clutter mishmash data to a program, it will either error out or provide inaccurate results. This fundamental principle has not changed in machine learning programming. Moreover, it has become more relevant over time, considering the massive amount of data required to train a model for real-life artificial intelligence use cases.

'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be predicted by exploiting their correlation with observed signals acquired from other sensors. For humans, our hands move synchronously with our legs, and we can exploit these implicit correlations for predicting new poses and for generating new natural-looking walking sequences. We can also go much further and exploit this form of transfer learning, to develop new control schemas for robust control of rehabilitation robots. In this short paper we introduce coregionalised locomotion envelopes - a method for multi-dimensional manifold regression, on human locomotion variates. Herein we render a qualitative description of this method.