Computer-assisted language interpretation is an increasingly promising field, leveraging Natural Language Processing (NLP) to handle spoken and written data. This research introduces a method tailored for discovering valuable skills within Bangla intelligent voice assistants. The primary objective of language processing, whether in voice or text form, remains understanding underlying information. Our method entails creating an artificial intelligence system that deduces meanings from input formats. Although there are already Bangla voice assistants available, our approach improves accuracy substantially. Interestingly, we concentrate on identifying negative comments and train our model to react correctly in those kinds of situations-a domain that hasn't gotten much attention in voice command interpretation. Our model demonstrates its ability to accurately estimate output, achieving a precision rate of 96% with a loss of about 5%. This study aims to bridge the existing gap in understanding and enhance knowledge within the specific domain of contextual information accessibility for individuals who are blind. Our proposed solution not only facilitates comprehension of audible negative contexts as exceptions but also effectively addresses complex navigation challenges faced by blind users. Furthermore, the findings of this research highlight promising directions for future exploration in this field.