Date of Award
Master of Science in Information Technology Leadership (MS ITL)
In a typical legal work environment, attorneys work with their staff to generate and send case related legal documents and communications. Traditionally the attorney will dictate to a device capable of recording audio and the legal assistant will transcribe the audio directly from the source. In the early days of recorded dictation audio was recorded and saved to analog tape. Once the technology became available, dictation was saved digitally to flash memory and transmitted to hard disk for playback by the legal assistant. It has been this way for years, and due to advances in voice recognition technology and computer processing there are alternative options to the traditional dictation/transcription process.
The focus of this paper is to examine the traditional process of dictation/transcription and how it compares to the process of using voice recognition software. Analysis of each process as well as an evaluation of voice recognition software will be performed. The document generation process will also be examined as it relates to transcription and creating a document, regardless of the content. The most efficient solution which benefits a small to medium size law firm will be recommended. According to Understanding How Law Offices Do Business, a small law firm has between one and ten lawyers and a mid-size law firm has up to 50 lawyers. These firms are the target audience.
The goal of this paper is to determine if the use of voice recognition software can help an attorney and their staff be more efficient, and if so, which voice recognition software and methods work the best. Tests will be performed analyzing both Dragon Naturally Speaking 12 Professional and Windows 7 voice recognition software on the desktop. The software with the higher accuracy rate based on our tests will be used to evaluate voice recognition processes throughout this paper.
Solomon, David and Pillar, Brian, "Voice Recognition and Mobility in the Legal Industry" (2015). Mathematics and Computer Science Capstones. 22.