Transcribing sessions and lectures with speaker identification

The problem

In any long session — a lecture, a meeting, or a court hearing — the raw recording isn't directly usable: half of a ninety-minute recording may be silence and side talk, and it's hard to go back and find a specific point. Handwritten minutes depend on one person's accuracy in hearing and writing, and in courts that's not a minor detail but an official document. A recording only becomes useful after it's cleaned, split by speaker, and summarized — work that's usually not done by hand after every session.

How it works

1. RegistrationTwo digital microphones record audio with enough quality to convert it to text.
2. Audio cleanupVoice activity detection removes silence and noise, greatly shortening the recording.
3. Speaker identificationThe system splits the recording by speaker, separating the lecturer's speech from everyone else's.
4. TranscriptionEach segment is converted into text attributed to its speaker, with its timestamp in the recording.
5. SummarizationAn AI model extracts the key points, decisions, and tasks.
6. DeliveryThe person in charge reviews and distributes the output: a cleaned recording, and minutes or a summary as printable slides.

Project features

• The cleaned recording cuts ninety minutes down to about half an hour of actual speech — an instantly tangible benefit.
• Speaker identification separates the main speaker from interventions, so you know exactly who said what.
• It serves three sectors with one product: universities, courts and law firms, and companies.
• The slides output is printable, so students can study from it directly.
• The two microphones allow the sound's direction to be determined, which a phone can't do.
• The person in charge reviews the output before it's distributed, so nothing wrong is published automatically.

Expected challenges

These are the main obstacles to expect during the build, laid out clearly so you know them before you start.

• Scientific terms and names need a custom dictionary for each subject.
• Speaker identification accuracy drops when voices overlap or sound alike.
• Echo in large halls distorts the sound before it reaches the model.
• Non-Jordanian dialects require expanding the training data.
• Recording privacy is a sensitive issue, and in court sessions it's subject to strict legal controls.

The consultation gives you a solution for each one.

Expected committee questions

Prepare your answers in advance: these questions come up often in defenses of similar projects.

1. How does the system distinguish between speakers, and which model is used?
2. How was the model trained on the Jordanian dialect, and where did the data come from?
3. What is the transcription word error rate (WER), and how was it measured?
4. How is recording privacy handled, especially in legal sessions?

Practice the defense, question by question