Automatic speaker identification for any audio or video file.

What is speaker identification?

Speaker identification is the process of detecting and labeling each distinct voice in an audio recording. The output is a transcript where every line is attributed to a specific speaker, often combined with timestamps so you know exactly when each person spoke.

This is also called speaker diarization. It answers the question "who spoke when?" rather than just "what was said?" For any recording with more than one person, a diarized transcript is far more useful than a plain text block.

How AI diarization works

Modern diarization systems analyze the acoustic properties of a recording to segment it into regions where a single speaker is active. Each segment is then grouped with other segments that share the same voice characteristics. The result is a set of speaker clusters, each assigned a label.

• Voice activity detection. the system first identifies which parts of the audio contain speech versus silence or background noise.
• Speaker segmentation. the audio is split at points where the speaker changes.
• Speaker clustering. segments from the same voice are grouped together under one label.
• Transcript alignment. the speech-to-text output is matched to the speaker segments, producing a labeled transcript.

Speaker identification vs. speaker verification

These two terms are often confused. Speaker identification asks "which of these known or unknown voices is speaking?" without requiring a pre-registered voiceprint. Speaker verification asks "is this person who they claim to be?" and compares against a stored reference.

Vook performs speaker identification and diarization: it detects and labels voices without needing any prior enrollment. This is the right tool for transcription, journalism, research, and content production. Speaker verification is a separate biometric use case.

What affects accuracy

Vook reaches up to 99% accuracy on clear audio in supported languages. Speaker separation quality depends on several factors:

• Audio clarity. recordings made in quiet environments with a good microphone produce the best results.
• Voice overlap. simultaneous speech makes it harder to separate speakers cleanly. The built-in editor lets you correct any misattributions.
• Number of speakers. files with two or three distinct voices are easier to diarize than large group recordings.
• Recording quality. low-bitrate phone calls or heavily compressed files reduce accuracy. WAV or FLAC files give the best results.

How to get the best results with Vook

A few simple steps before uploading can significantly improve speaker identification quality. Record in a quiet space, use a dedicated microphone rather than a built-in laptop mic, and avoid having multiple people speak at the same time.

• Choose a high-quality format. WAV or FLAC preserves audio detail that helps the diarization engine separate voices accurately.
• Use the built-in editor. after processing, rename Speaker 1, Speaker 2, etc. to real names, merge any incorrectly split segments, and mask sensitive information before exporting.
• Try Vook Chat. once the transcript is labeled, use Vook Chat to extract quotes per speaker, summarize each person's contributions, or identify key themes across the conversation.

Privacy and compliance considerations

Speaker identification involves processing voice data, which is classified as biometric data under GDPR. Choosing a processor that is GDPR-native and hosted in the EU is essential for organizations subject to European data protection law.

Vook stores all files on servers in France, encrypts data with AES-256 at rest, and deletes audio files automatically after 7 days. Your recordings are never used to train AI models and are never shared with third parties. A Data Processing Agreement is available on request for organizations that need formal documentation.