SpecAugment für Spracherkennungsquiz

Während Sie am Quiz teilnehmen, werden Antworten und Erklärungen angezeigt.

1. Womit arbeitet SpecAugment?
   • Das Log-Mel-Spektrogramm
   • Die rohe Audiowellenform
   • Das Texttranskript
   • Die Gewichte des Modells
2. Welche davon ist eine der drei Operationen von SpecAugment?
   • Tonhöhenumkehr
   • Frequenzmaskierung
   • Wortersetzung
   • Echoinjektion
3. What role did the SpecAugment paper assign to its feature transformations?
   • They guarantee that every recognizer avoids overfitting
   • They replace the need for transcribed training data
   • They augment ASR training inputs and were evaluated for recognition performance
   • They improve every speech model by the same amount
4. Was bewirkt „Zeitmaskierung“?
   • Löscht die Zeitstempel des Transkripts
   • Beschleunigt die Audiowiedergabe
   • Entfernt die Stille am Anfang
   • Löscht eine Spanne aufeinanderfolgender Zeitschritte
5. How are time- and frequency-mask locations chosen during SpecAugment training?
   • They are randomly sampled, so the same input may receive different masked regions on later passes
   • They are fixed permanently before the training set is collected
   • They are chosen by a language model after each recognition result
   • They are identical for every utterance and every training step
6. Bei welchem Benchmark hat SpecAugment besonders dazu beigetragen, hochmoderne Ergebnisse zu erzielen?
   • ImageNet
   • LibriSpeech
   • SQuAD
   • KLEBER
7. What does time warping do in the original SpecAugment policy?
   • It replaces speech frames with translated text
   • It masks every frequency channel permanently
   • It deforms feature positions along the time axis
   • It changes the speaker identity in the waveform
8. Which recognizer architecture did the original SpecAugment paper use in its experiments?
   • A region-based image detector
   • A music source-separation U-Net only
   • A text-only masked language model
   • Listen, Attend and Spell (LAS)