Acoustic side-channel attacks on keyboards can bypass security measures in many systems that use keyboards as one of the input devices. These attacks aim to reveal users’ sensitive information by targeting the sounds made by their keyboards as they type. Most existing approaches in this field ignore the negative impacts of typing patterns and environmental noise in their results. This paper not only seeks to address these shortcomings by proposing an applicable method that takes into account the user’s typing pattern in a realistic environment, but it also uses the differences in typing patterns to launch the attack. Our method achieves an average success rate of 43% in detecting average-length words across all our case studies, when considering typists in an individual office setting without any restrictions on their typing style. Achieving such a high success rate has not been reported without imposing significant restrictions on environmental noise, recording devices, and typing styles.