Parse meeting transcripts by speaker

To parse meeting transcripts by speaker, you can follow these steps:

1. Extract Speaker Information

Identify and extract the speaker labels from the transcript. This could be in the form of speaker names (e.g., “John”, “Anna”) or titles (e.g., “Manager”, “Employee”). If the transcript has timestamps, they can also help in distinguishing when each speaker is talking.

Example:

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[John] Good morning everyone, let's get started with the meeting.
[Anna] I think we should discuss the budget first.
[John] That sounds good. I have some updates on that.

2. Separate Transcript by Speaker

After extracting the speaker labels, segment the transcript so that all dialogue from a single speaker is grouped together. Each speaker’s part should be organized into a separate block for clarity.

Example output:

• John:

  • Good morning everyone, let’s get started with the meeting.

  • That sounds good. I have some updates on that.

• Anna:

  • I think we should discuss the budget first.

3. Handle Special Cases

Some transcripts may have overlaps (e.g., both speakers talking at once) or interruptions. Ensure these moments are handled correctly, either by indicating both speakers in a single segment or by identifying overlapping speech with clear timestamps.

4. Optional: Time-Based Segmentation

If you need to split the transcript further based on timestamps, you can organize each speaker’s dialogue under the corresponding time sections. For example:

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[00:00] John: Good morning, let's begin the meeting.
[00:05] Anna: Let's talk about the budget first.
[00:10] John: Sure, I have some updates for that.

5. Automated Parsing Using Tools

If you’re working with a large volume of transcripts or need automation, you can use natural language processing (NLP) libraries or transcription services to perform speaker diarization (automated speaker identification) and parsing:

• Python libraries like pyAudioAnalysis or speech_recognition can assist in transcribing and identifying speakers.

• Google Cloud Speech-to-Text and Amazon Transcribe also offer speaker diarization features.

Would you like help with a specific method or tool to parse your transcripts?