Wav2Letter 畳み込み ASR クイズ

クイズに答えると、答えと解説が表示されます。

1. Which architecture defines the original wav2letter acoustic network?
   • A recurrent-only network
   • A decision-tree ensemble
   • A Gaussian-mixture-only acoustic network
   • A convolutional network without recurrent acoustic connections
2. Which research organization is associated with the original 2016 wav2letter paper?
   • Google 脳
   • Microsoft 研究
   • Facebook AI Research
   • NVIDIA
3. Which output representation does the original wav2letter acoustic model predict from its studied speech inputs?
   • Only an intermediate human-labeled phoneme transcript
   • Letters or graphemes
   • Only a list of speaker names
   • Only a global tempo value
4. 元の AutoSegCriterion (ASG) 損失は、より一般的な CTC 損失とどのように異なりますか?
   • 空白記号を削除し、文字の遷移を直接モデル化します。
   • フレームごとに手動で位置を調整する必要があります
   • RNN でのみ動作します
   • 可変長シーケンスは処理できません
5. What computation difference does a convolutional acoustic stack offer compared with a conventional recurrent dependency across time?
   • Every complete speech decoder becomes fully parallel
   • No labeled transcription data is needed for its supervised objective
   • Convolution operations can be evaluated across positions without that recurrent chain
   • Every workload necessarily obtains zero transcription error
6. Which C++ machine-learning library documents ASR applications formerly associated with the wav2letter project?
   • scikit-learn only
   • An audio-compression format
   • A required spelling dictionary standard
   • 懐中電灯
7. Which tensor library does the later wav2letter++ paper identify for its C++ framework?
   • ArrayFire
   • A required Java virtual machine
   • Only the original Torch7 runtime
   • A fixed spreadsheet engine
8. For two length-three temporal convolutions at stride one and dilation one with no padding or intervening operation, how many original positions can one second-layer output depend on?
   • 3
   • 5
   • 6
   • ナイン