Speaker Diarization Quiz

Answers and explanations appear as you take the quiz.

1. Speaker diarization is often summarized by which question?
   • What words were spoken?
   • What language is being used?
   • Who spoke when?
   • What emotion is in the voice?
2. What is a speaker embedding?
   • A transcript of the speech
   • A timestamp marking silence
   • A compression format for audio
   • A fixed-length vector representing a voice's characteristics
3. Which metric is commonly used to evaluate diarization systems?
   • Diarization Error Rate (DER)
   • Word Error Rate (WER)
   • Signal-to-Noise Ratio (SNR)
   • Frame Rate
4. Does standard diarization need to know speakers' real names in advance?
   • Yes, names are required as input
   • No, it assigns anonymous labels like Speaker 1 and Speaker 2
   • Yes, but only the first speaker
   • No, it always outputs full identities
5. What problem does permutation-invariant training solve in end-to-end neural diarization?
   • It removes the need for an audio signal
   • It evaluates speaker outputs over permutations so arbitrary output-label order does not affect training
   • It identifies a person’s legal name from their voice
   • It forces the model to use exactly one speaker channel
6. Which step typically comes first in a classic diarization pipeline?
   • Clustering embeddings into speakers
   • Assigning real names
   • Translating the audio
   • Voice activity detection to find speech regions
7. Which error categories commonly make up Diarization Error Rate?
   • Missed speech, false alarm speech and speaker confusion
   • Word error, punctuation and translation error
   • Only missed speech
   • Pitch, loudness and speaking rate
8. How can an end-to-end diarization model represent simultaneous speakers?
   • By forcing every frame to contain exactly one speaker
   • By predicting multiple active speaker labels for the same time region
   • By assigning real names to all voices
   • By deleting overlap before analysis