DeepSpeech Architecture Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Ndeipi basa rekurasikirwa rinobvumira DeepSpeech kudzidzisa pasina furemu-level kurongeka pakati pekuteerera uye zvinyorwa?
   • Kureva kukanganisa kwakapetwa kaviri
   • Yambuka-entropy pamusoro pemahwindo akareba-akareba
   • Connectionist Temporal Classification (CTC)
   • Kurasikirwa katatu
2. Chii chaive chikuru hunyanzvi hwekuvaka iyo DeepSpeech yakakurumbira?
   • Kupera-kusvika-kumagumo kudzidziswa kweimwe neural network kubva kuodhiyo maficha kune zvinyorwa
   • Kupatsanura kuzivikanwa mune zvakawanda zvakagadzirwa nemaoko, zvikamu zvakasiyana
   • Kushandisa chete mutemo-based phonetic dictionaries
   • Kuziva kutaura chete netemplate yekufananidza
3. Sei DeepSpeech ichishandisa bidirectional recurrent layer?
   • Kudzvanya saizi yefaira rekuteerera
   • Kupa mamiriro ekufungidzira ega ega kubva kumafuremu apfuura neachauya
   • Kuturikira pakati pemitauro
   • Kubvisa kudiwa kwechero data yekudzidziswa
4. Ndeupi rudzi rwekupinza maficha ayo DeepSpeech inowanzoshanda pairi?
   • Raw pixel data
   • Zvinyorwa zvakafanonyorwa
   • Yakadzvanywa MP3 metadata tags
   • Spectrogram kana MFCC maficha akaverengerwa pamusoro pemafuremu mapfupi ekuteerera
5. Chii chinowanzowedzerwa panguva yedecode kuti uvandudze DeepSpeech's izwi sarudzo uye zviperengo?
   • Muenzaniso wemutauro wekunze (semuenzaniso, n-giramu modhi)
   • Maikorofoni yechipiri
   • Mugadziri wemufananidzo mukuru
   • A hardware encryption chip
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