Speaker Identification

Learn how automatic speaker identification works, when to use this feature, how to optimize accuracy, and troubleshoot common issues with voice separation in your transcriptions.

How Identification Works

When to Use Speaker Identification

Work Meetings

Separate each participant's speech.

Benefits:

• Better organized meeting minutes
• Easy identification of who said what
• Improved meeting reports
• Clear accountability for decisions

Interviews and Podcasts

Distinguish between interviewer and interviewee.

Benefits:

• Transcriptions ready for publication
• Clear separation of questions and answers
• Easier post-editing
• Improved content readability

Classes and Lectures

Identify the instructor and participants.

Benefits:

• Separation of main content and questions
• Identification of interaction moments
• Better organization of educational material
• Easier content review

Customer Service

Differentiate between agent and customer.

Benefits:

• Service quality analysis
• Training based on real conversations
• Speaking time metrics for each party
• Compliance and auditing

How to Optimize Accuracy

Audio Quality (Impact: Very High)

• Use individual microphones when possible
• Avoid excessive echo and reverberation
• Keep volume balanced between speakers
• Avoid overlapping voices (speaking at the same time)

Number of Speakers (Impact: High)

• 2-4 speakers: maximum accuracy (90-95%)
• 5-6 speakers: good accuracy (80-90%)
• 7+ speakers: reduced accuracy (70-80%)
• Provide the approximate number if known

Duration and Pauses (Impact: Medium)

• Utterances of at least 3-5 seconds are better identified
• Pauses of 1-2 seconds help with separation
• Avoid very frequent interruptions
• Longer files have better overall accuracy

Settings (Impact: Medium)

• Enable identification only when needed
• Use on files with clearly distinct multiple speakers
• Consider additional cost vs. benefit
• Test with a small sample first

Common Issues and Solutions

Speakers are not correctly distinguished

Possible causes:

• Very similar voices (siblings, family)
• Poor audio quality or noise
• Frequent overlapping speech
• Single microphone for multiple people

Solutions:

• Check if the voices are truly distinct
• Improve original audio quality
• Re-record with separate microphones if possible
• Use simple transcription if identification fails
• Edit manually after transcription

Too many false speakers identified

Possible causes:

• Background noise being interpreted as voice
• Echo or reverberation
• Overlapping music or sounds
• Inconsistent audio quality

Solutions:

• Clean audio of noise before uploading
• Use a recording from a quieter environment
• Remove background music if possible
• Adjust sensitivity settings
• Re-upload with clean audio

One speaker is split into multiple

Possible causes:

• Abrupt changes in tone or volume
• Audio with cuts or edits
• Unstable connection during online calls

Solutions:

• Normalize the audio volume
• Use an unedited file when possible
• Record locally instead of over the network