quiz bu lëkkale ak tegtal · Diggante Niveau

X-Vector Oparlër Quiz

Xoolaat màndarga dàntite vecteur x, muy tekki ci diir bu TDNN, dajale lim, dugal ay couche yu nëbbu, ak pexe yu ñuy joxe poñ ngir méngale waxkat yi.

Yooni njiit yu jëm ciX Vector Oparleur
Laaj 1 de 8

What information is a speaker-recognition x-vector designed to emphasize?

Xoolaatal laaj bu nekk8 laaj

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

  1. What information is a speaker-recognition x-vector designed to emphasize?
    • Only the exact words spoken as text
    • Only the recording's file format
    • A guaranteed identity proof unaffected by recording conditions
    • Speaker-related characteristics rather than a transcript of the utterance
  2. Ban couche mooy soppi wax ju am guddaay bu mën soppiku nekk benn vecteur bu am guddaay bu takku ci reso vecteur x bi?
    • Seggal nataal buñ jagleel
    • Ab layer buy boole lim ak xayma moyenne ak standard deviation ci diir bi
    • ab couche dropout
    • Softmax bu mujj buñu jëfandikoo ngir dugal ci boppam
  3. Ban xeetu reso neuronal lañuy faral di jëfandikoo ngir génne ay vecteur x?
    • Reseau adversaire buy jur
    • Ensemble garabu dogal
    • Reseau neuronal buy yeexal waxtu (TDNN)
    • Regresioŋ ligneer bu leer
  4. naka lañuy méngale ñaari vecteur x ngir xam ndax ci benn waxkat lañu bawoo?
    • ci nuru cosinus wala backend PLDA
    • Ni ñuy lim kàddu yu méngoo
    • Sooy méngale dayo fichier yi
    • Soo xoolee tolluwaayu misaalu audio bi
  5. Which established representation supplied comparison baselines in the 2018 x-vector speaker-recognition study?
    • A visual image-token embedding
    • A text-only word-count vector
    • i-vecteur
    • An audio file-size histogram
  6. Ban architecture bu bees moo gëna xéewale ci vecteur x bu yàgg bi am chaine attention ak statistique pooling?
    • ResNet-50 ngir nataal yi
    • GPT-2
    • YOLO
    • ECAPA-TDNN
  7. Why is the classic x-vector extracted from a hidden segment-level layer rather than used as the final training-speaker softmax output?
    • The hidden representation can be compared as an embedding instead of being limited to probabilities for the training speaker labels
    • The hidden layer contains the recording's text transcript
    • Softmax probabilities are the only place where acoustic information exists
    • Hidden layers cannot process an unseen recording
  8. What does a time-delay layer contribute before statistics pooling in an x-vector extractor?
    • It stores only a single final filename
    • It combines information from neighboring acoustic frames, building temporal context
    • It assigns a new random speaker label to every frame
    • It removes all time information before any acoustic processing