LIDAR technology is able to replace inaccurate and time-consuming manual measurements of tree dimensions in forest inventory fieldwork assessment. Additional data-collection (quality of stems, regeneration) is needed and should be preferably performed while collecting the point-cloud by a backpack SLAM device. This study introduces a method that combines voice input and LiDAR scanning technology for optimized forest inventory. LiDAR technology can extract single tree parameters, but tools for tree species recognition and damage assessment are still missing. The study uses the Stonex X120go backpack scanner with a rotating Hesai Pandar 32-channel sensor to collect point-clouds, which are less noisy compared to similar SLAM-devices. The method combines speech input and laser scanning technology to determine tree species and other properties such as damage survey and tree quality. The user can identify tree species by speaking while walking through the forest with the scanner, and the voice recording is done by a mobile phone or tablet with real-time conversion into database entries. After post-processing the point-cloud, the high-resolution trajectory can be synchronized with the voice-based database entries. The data collected with the laser scanner can be processed by AI technology to automatically identify tree species by considering the voice input and extracting quantitative tree parameters. Different approaches will be investigated to ensure accuracy in assigning spoken quantitative tree information. The results of the DBH evaluation of the scans, as well as the coordinates from the trajectory, are joined with the speech input data in a database using a script that utilizes the timestamp or GPS coordinates. The combination of speech input and laser scanning technology provides an efficient and precise method for forest inventory. Overall, the study concludes that the combination of voice input and LiDAR scanning technology provides an operational and optimized forest inventory process.