Speech is a convenient hands-free communication channel where humans are already experienced users. It can implicitly create trustfulness between two operators and lead to a comfortable and natural collaborative environment. As stated in existing literature, speech interaction could increase efficiency and improve certain aspects of Human-Robot Collaboration (HRC). Anyway, speech recognition in industrial scenarios presents different challenges: the typical noisy environment can affect dramatically the interaction performance, leading to an unacceptable inaccuracy in understanding the uttered intention.In this work, we propose and evaluate a modular system for robust and natural speech interaction in challenging acoustical environments. The system has been integrated and tested in a realistic HRC scenario in which the acoustic interaction and efficiency have been evaluated. The developed framework focuses on decreasing the requirements in terms of signal-to-noise ratio, providing a methodology to evaluate the naturalness of the interaction and improvements in efficiency.The solution is designed with a modular approach, providing an easy configuration for ROS-based systems. In this way, it allows a simple integration in existing applications and future research projects, where a dual speech-based interaction can increase the overall performance of the HRC.