## 1 Introduction  
This paper investigates the effects of varying noise levels and varying lighting conditions on practical speech and gesture control communication/interaction, within the context of an aerial robot (aerobot) application. This paper is part of a series of research work investigating the use of novel human-computer interaction (HCI) interfaces in the control of small multirotor unmanned aerial vehicles (UAVs), with a particular focus on the multimodal speech and visual gesture (mSVG) interface [1; 2; 3; 4]. The aim was to determine the practical suitability of the multimodal combination of speech and visual gesture in human-aerobotic interaction by investigating the limits of the individual components of speech and gesture through the introduction of noise--corrupting sound and lighting conditions. Known limitations of the proposed system, from previous studies, suggests that (1) the mSVG method could be susceptible to speech corruption during capture, due to the noise generated by the multirotor propulsion systems and other loud ambient noise such as in stormy weathers, and (2) poor visibility levels could affect the visual gesture capture, as may be the case at night, or in cloudy or misty weather, although these effects were not quantified. Therefore, the extent of this limitation is being practically measured, in order to inform the possibility of developing techniques that could either extend the range of the mSVG method's usefulness or develop a way of working around its limitations.  
A computer-based hardware-in-the-loop lab experiment was designed with 37 human participants, in order to (1) determine the effect of varying noise levels (55 dB to 85 dB) on speech recognition, and (2) the effect of varying lighting levels (10 Lux to 1400 Lux) using different lighting colour temperature of white (5500 K) and yellow (3500 K), and a mixture of these colour temperatures with different capture backgrounds, on visual gesture recognition. This study builds on the progress from a previous work by [3]. Unlike in the previous work by [3], where only five participants were used in the study, we have recruited 37 participants and have a broader set of data to draw and generalise conclusions from. This work validates and advances the work done in [3] further by performing more in-depth and multi-dimensional analysis on a larger number of subjects. It was observed that, as noise level increases, the speech command recognition rate drops. A regression characteristic model was developed for the custom CMU Sphinx automatic speech recogniser showing speech control command performance for each ambient noise level ranges of 55 dB to 85 dB. The upper limit of the custom developed CMU Sphinx based speech interface was determined to be 75 dB with a 65% recognition rate. Beyond this threshold, speech control was considered not practical due to the significantly high rate of control failure. This limit was well within the practical operation limit of the HoverCam UAV propulsion noise level, but not the DJI Phantom 4 Pro. Some ways of improving this limits were suggested, such as investigating other speech recognisers with a different model other than the hidden Markov model, which was used in this particular study. From the varying lighting experiment, it was clear that background and lighting levels only had a very little effect on gesture quality, and that the gesture capture system and processing methods were more critical to the successful recognition of gesture commands.