Mutauri Diarization Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Mutauri diarization inowanzopfupikiswa nemubvunzo upi?
   • Mashoko api akataurwa?
   • Ndeupi mutauro uri kushandiswa?
   • Ndiani akataura rini?
   • Inzwi rakaita sei?
2. Chii chinonzi simbirira mutauri?
   • Chinyorwa chekutaura
   • Chitambi chinomaka kunyarara
   • A compression fomati yeaudio
   • A fixed-length-length vector inomiririra maitiro ezwi
3. Ndeipi metric inowanzoshandiswa kuongorora diarization masisitimu?
   • Diarization Error Rate (DER)
   • Chiyero chekukanganisa kwemazwi (WER)
   • Signal-to-Noise Ratio (SNR)
   • Frame Rate
4. Ko standard diarization inoda kuziva mazita evatauri chaiwo pamberi?
   • Hongu, mazita anodiwa sekuisa
   • Kwete, inopa mazita asingazivikanwe seMutauri 1 uye Mutauri 2
   • Hongu, asi mutauri wekutanga chete
   • Kwete, inogara ichiburitsa mazita akazara
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. Nderipi danho rinowanzouya pekutanga mune yekare diarization pombi?
   • Kubatanidza zvinomisikidza kuva vatauri
   • Kupa mazita chaiwo
   • Kuturikira audio
   • Kuonekwa kwezviitwa nezwi kuti uwane nzvimbo dzekutaura
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