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.

Role: User Experience, Visual Design and Prototyping

Challenges

Ensuring accuracy: One of the biggest challenges was ensuring that the app provided accurate transcriptions. The app needed to be able to handle different accent.

User experience: Another challenge was designing an app that provided a seamless user experience. The app needed to be intuitive and easy to use.

Scalability: The app needed to be able to handle large volumes of data, including audio and video files, and transcription requests. This required a cloud-based infrastructure.

Integration: The app needed to be able to integrate with other tools and services, such as cloud storage services and text editors. his required developing APIs.

Research:

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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.

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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.

Based on the user personas and market research, I developed a set of features for the app, including:

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• Audio and video file upload

• Automated transcription service

• Editable transcripts

• Integration with other tools and services

• Affordable pricing model

• Mobile app support