Sheekada DeepSpeech Kediska Dhismaha

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay shaqada luminta u oggolaanaysa DeepSpeech in ay tababarto iyada oo aan la isku toosin heerka qaab-dhismeedka maqalka iyo qoraalka?
   • Khalad afar gees leh celcelis ahaan
   • Iskutallaabta-entropy korka daaqadaha dhererka go'an
   • Kala-saar ku-meel-gaar ah oo ku-meel-gaar ah (CTC)
   • Luminta saddex-geesoodka
2. Maxay ahayd falsafada dhismaha ee ugu weyn ee DeepSpeech faafisay?
   • Tababarka dhamaadka-ilaa-dhamaadka ee hal shabakad neerfaha laga bilaabo astaamaha maqalka ilaa qoraalka
   • U kala qaybinta aqoonsiga qaybo badan oo gacanta lagu farsameeyay, oo kala gooni ah
   • Isticmaalka qaamuusyada dhawaaqa ku salaysan xeerka
   • Aqoonsiga hadalka oo keliya oo la jaanqaadaya template
3. Waa maxay sababta DeepSpeech u isticmaasho lakab laba jiho ah oo soo noqnoqda?
   • Si loo cadaadiyo cabbirka faylka maqalka ah
   • Si aad u bixiso saadalin kasta macnaha guud ee hore iyo mustaqbalka labadaba
   • Si loo turjumo luqadaha dhexdooda
   • Si meesha looga saaro baahida xogta tababarka
4. Waa maxay nooca wax gelinta ee uu DeepSpeech caadi ahaan ku shaqeeyo?
   • Xogta pixel ceeriin
   • Qoraallada qoraalka ee horay loo qoray
   • MP3 metadata tags la cufay
   • Spectrogram ama MMFC sifooyinka lagu xisaabiyay fiimyada maqalka gaagaaban
5. Maxaa inta badan lagu daraa wakhtiga go'aaminta si loo hagaajiyo doorashada kelmad DeepSpeech iyo higaada?
   • Nooca luqadda dibadda ( tusaale, qaabka n-gram)
   • Makarafoonka labaad
   • Kala soocida sawirka weyn
   • Qalab sireed hardware ah
6. What does the original DeepSpeech output layer predict at each acoustic time step?
   • A fixed speaker-identity vector only
   • A single complete translated document with no time steps
   • A probability distribution over character labels and the CTC blank
   • The recording's filename and compression format
7. How does CTC relate an acoustic-frame label path to the final DeepSpeech transcript?
   • It sorts all character labels alphabetically
   • It requires exactly one audio frame for each word
   • It keeps every repeated frame label as a separate written character
   • It collapses repetitions and removes blank symbols according to the CTC rule
8. Why does original DeepSpeech's bidirectional acoustic context matter when designing a live recognizer?
   • Bidirectional recurrence needs no future observations
   • Future-frame context requires lookahead or waiting, so it is not automatically zero-lookahead streaming
   • The network can know later audio before it is recorded
   • A character language model removes all buffering requirements