Guide-yakabatana mibvunzo · Pakati Nhanho

RNN-Transducer Models Quiz

Understand how RNN-Transducer models combine acoustic frames and output history for streaming sequence transcription tasks.

Nzira dzekutungamira dzinoenderanaRnn Transducer Models
Mubvunzo 1 ye 8

Ndeipi mipimo yeCTC inotariswa neRNN-Transducer?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ndeipi mipimo yeCTC inotariswa neRNN-Transducer?
    • CTC's conditional-independence fungidziro, iyo inodzivirira modelling inobuda-tokeni kutsamira.
    • CTC haikwanise kugadzirisa odhiyo zvachose
    • CTC inoda maGPU kumhanya
    • CTC inoshanda pamifananidzo chete
  2. Ndezvipi zvinhu zvitatu zvakakosha zveRNN-Transducer?
    • Encoder, decoder, uye kusarura
    • Encoder (transcript network), kufanotaura network, uye network yakabatana
    • Tokenizer, embedder, uye softmax chete
    • Misoro miviri yekutarisisa uye router
  3. Ndeipi basa rinoitwa netiweki yekufungidzira muRNN-T?
    • Iyo inomanikidza iyo audio waveform
    • Inoyera maikorofoni volume
    • Inoita senge modhi yemutauro, inomisikidza pamatokeni akamboburitswa
    • Inosarudza sampling rate
  4. Nei RNN-T ichitsigira yakaderera-latency kutepfenyura zvakadaro?
    • Inogadzirisa kutaura kwese kamwechete isati yaburitsa chero chinhu
    • Iyo inofuratira kurongeka kwenguva zvachose
    • Inoshanda chete pamafaira akarekodhwa
    • Kurongeka kwayo ndeye monotonic uye kuruboshwe-kuenda-kurudyi, inofambira mberi kuburikidza neaudio ine blanks
  5. MuRNN-T decoding, kuburitsa chiratidzo chisina chinhu kunoita sei?
    • Kufambira mberi kune inotevera yekuteerera nguva nhanho
    • Inopedza kunyorwa kwese
    • Inodzima izwi rapfuura
    • Kuchinjira kune mumwe mutauro
  6. Ndeipi encoder mhando inowedzera kutsiva LSTMs mune yemazuva ano yekugadzira RNN-T masisitimu?
    • Sarudzo miti
    • Conformer encoders
    • Plain bag-of-words
    • Akavanzika Markov Models
  7. What two histories condition the RNN-T joint network’s output distribution?
    • Only the future transcript and a speaker photo
    • Only the last acoustic frame, without output context
    • The encoder representation of acoustic input and the prediction-network representation of prior output labels
    • The optimizer state and learning rate
  8. What alignment behavior does an RNN-T allow before advancing to the next audio frame?
    • It must emit exactly one label for every frame
    • It advances time only after a word-final token
    • It generates all output before reading any audio
    • It can emit zero or more nonblank labels at the current frame, then a blank advances time