Transcribe AI (Speech to Text App)
User Experience Design
The aim of this case study is to outline the process of designing an AI speech to text transcription app called "Transcribe AI Speech to Text." The app is intended to enable users to easily convert audio and video files into text format using an automated transcription service, allowing them to edit and share the resulting transcripts.
Client:
Martin Marinov
Role:
UX / UI Designer
Year:
2020
Challenge
Ensuring accuracy in the app's transcriptions was a significant challenge, requiring robust handling of diverse accents to deliver reliable results. Designing a seamless user experience was equally critical, demanding an intuitive and user-friendly interface to enhance usability. Scalability posed another hurdle, necessitating a cloud-based infrastructure to efficiently manage large volumes of audio, video, and transcription requests. Additionally, integration with external tools like cloud storage and text editors required the development of effective APIs to ensure smooth interoperability.
Objective
The research phase began with a thorough examination of the current market for speech to text transcription apps. This involved researching existing apps, analyzing their features, and identifying their strengths and weaknesses. The research revealed that many existing apps were either too complex, too expensive, or had limited functionality. There was a clear opportunity to develop an app that provided users with a simple, affordable, and efficient transcription service. The research also highlighted the importance of accuracy when it comes to speech to text transcription. While many apps claim to provide accurate transcription, the reality is that even the most advanced machine learning algorithms can struggle with certain accents, background noise, and other factors that can impact the clarity of the audio. As a result, the app design needed to prioritize accuracy, ensuring that users could rely on the resulting transcripts for important tasks such as note-taking, meeting minutes, and more.
Results
Based on user personas and market research, the "Transcribe AI Speech to Text" app was designed with a set of key features to meet user needs effectively, including audio and video file upload capabilities, an automated transcription service for efficient conversion to text, editable transcripts for user flexibility, integration with external tools and services like cloud storage and text editors, an affordable pricing model to ensure accessibility, and mobile app support for on-the-go usability. Then I began working on wireframes and mockups for the app, which helped to visualize the user experience and ensure that the app was intuitive and easy to use. The team also conducted several rounds of user testing, which provided valuable feedback and helped to identify areas for improvement. The result was an app that provided users with a simple, affordable, and accurate transcription service, helping them to convert audio and video files into text format quickly and easily. The apps success was due. Start-ups and companies reached out to me after I published this concept on different platforms.
