What is Speaker Diarization

How It Works

Speaker diarization systems segment audio into speaker-homogeneous regions through a pipeline of voice activity detection, speaker embedding extraction, and clustering. First, non-speech regions are filtered out. Then, short audio segments are encoded into speaker embeddings that capture voice characteristics. Finally, clustering groups segments from the same speaker together.

Technical Details

Modern systems use neural speaker embeddings (x-vectors, ECAPA-TDNN) with spectral clustering or agglomerative clustering. End-to-end neural diarization models (EEND) jointly perform segmentation and clustering. pyannote.audio is a popular framework providing pretrained diarization pipelines. Performance is measured using Diarization Error Rate (DER), which combines missed speech, false alarm, and speaker confusion errors.

Best Practices

• Use pretrained pipelines like pyannote.audio for robust out-of-the-box diarization
• Combine diarization with ASR to produce speaker-attributed transcripts
• Set the expected number of speakers when known, as it significantly improves accuracy
• Apply voice activity detection before diarization to handle silent segments

Common Pitfalls

• Not handling overlapping speech, which is common in conversations and meetings
• Assuming consistent audio quality throughout the recording
• Using diarization models trained on telephony data for far-field microphone recordings
• Not post-processing short speaker segments that result from clustering errors

Advanced Tips

• Use speaker diarization to create per-speaker indices for searching by voice identity
• Implement online diarization for real-time applications like live meeting transcription
• Combine diarization with emotion detection for speaker-attributed sentiment analysis
• Use speaker embeddings from diarization to build speaker identification systems for known speakers