Laajteb surnaalu waxinkat

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Ban laaj mooy faral di tënk surnaalu waxinkat bi?
   • Yan kàddu lañu wax?
   • Ban làkk lañuy jëfandikoo?
   • Kan moo wax kañ?
   • Lan mooy yëg-yëg ci baat bi?
2. Luy dugal waxkat?
   • Transkripsioŋ bu diskuur bi
   • ab timestamp buy màndargaal noppalu
   • Format kompresioŋ ngir audio
   • Vecteur bu am guddaay bu takku buy màndargaal màndarga baat
3. Ban metric lañuy gëna jëfandikoo ngir jàngat sistemu diarization yi?
   • Njuumteg surnaal (DER)
   • Tolluwaayu njuumte ci baat (WER)
   • Rasio siñaal ak bruit (SNR)
   • Taux de cadre
4. Ndax surnaal bi ñuy faral di def dafa wara xam tur wi dëggu ci waxkat yi ci kanam?
   • Waaw, tur yi lañuy laaj ngir dugal
   • Déedet, dafay joxe ay etiket yu nëbbu yu melni Waxkat 1 ak Waxkat 2
   • Waaw, waaye ki jëkka wax kese
   • Dédeet, saa yu nekk dafay génne dàntite yu mat
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. Ban jéego mooy njëkka am ci pipeline diarization bu yàgg bi?
   • Clustering yuñ samp ci biir kàddukat yi
   • Jox tur dëgg
   • Tekki audio bi
   • Gis liggéeyu baat ngir gis gox yi ñuy wax
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