Voice Assisted Key-In Building Quantities Estimation System

Voice recognition technology has been in existence over several decades but its application in the construction industry has been minimal. Despite the several advantages it offers, its application has been limited to smart building integration only. This study has made a significant contribution by integrating voice recognition technology into key-in building quantities estimation software. The Visual Basic programming language was used to design and code the interface of the voice recognition system and key-in estimating software model. The prototype model continues to have some challenges because it cannot work efficiently in a noisy work environment and there is limited range of vocabulary it can recognize. This paper seeks to challenge the stakeholders of the construction industry to maximize the benefits of voice recognition technology and integrate it into other construction tasks. In addition, future research can consider integrating building information modeling and voice recognition technology

Assessment of Classifiers for Potential Voice-Enabled Transportation Apps

Transportation apps are playing a positive role for today’s technology-driven users. They provide users with a convenient and flexible tool to access transportation data and services, as well as collect and manage data. In many of these apps, such as Google Maps, their operations rely on the effectiveness of the voice recognition system. For the existing and new apps to be truly effective, the built-in voice recognition system needs to be robust (i.e., being able to recognize words spoken in different pitch and tone). The goal of this study is to assess three post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy) to enhance the commonly used Google’s voice recognition system. The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual’s voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that post-processing techniques could significantly enhance Google’s voice recognition system