Voice Enabled Form Filling Using Hidden Markov Model

Speech Recognition technology is widely used for voice-enabled form filling. The manual process of filling out forms by typing has become increasingly challenging and time-consuming. This issue is particularly evident in various locations such as job applications and internships. To address this problem, a solution is proposed as a system that automates the form-filling process using speech recognition technology. The ability to operate anything with voice command is a crucial factor in today’s environment. The proposed system is that it automatically fills out the forms. i.e., the system analyses the user’s unique voice, identifies the user’s speech, and then transcribes the speech into text. This paper proposes a machine-learning model that builds on Hidden Markov Model. The model will be trained and tested on this system and the proposed pre-processed methodology is Mel Frequency Cepstral Coefficients. The methodology was widely used in the prospect of recognition of voice automatically. The results demonstrate that this system effectively accurately transcribes user speech into text, simplifying the form-filling process significantly. By providing these results, we hope to demonstrate how this technology has the potential to revolutionize data entry and accessibility while also establishing a strong case for speech recognition as a convenient way to speed up form completion.