Maswali yanayounganishwa na mwongozo · Kati Kiwango
Hotuba kwa Maswali ya Maandishi
Kagua hitilafu za manukuu, kasi ya makosa ya maneno, hali ya kelele, lebo za spika, kimya, mihuri ya muda na jinsi ya kukagua maneno muhimu dhidi ya sauti.
Swali 1 ya 8
A transcript has four substitutions, three deletions, and two insertions against a 100-word reference. What is its word error rate?
Hakiki kila swali8 maswali
Majibu na maelezo huonekana unapojibu maswali.
- A transcript has four substitutions, three deletions, and two insertions against a 100-word reference. What is its word error rate?
- 9%
- 4%
- 7%
- 100%
- Why inspect individual transcript errors after calculating word error rate?
- The score identifies speaker identity
- The aggregate does not show whether an error changed a key instruction
- The percentage verifies punctuation
- The number proves accent fairness
- A recording contains overlapping speakers and unusual names. What does the guide recommend?
- Treat speaker labels as verified identity
- Assume one dataset score applies to the recording
- Expect recognition challenges and review names and uncertain passages
- Remove the original audio
- A transcript labels two speakers. What does a speaker label establish?
- It shows that translation is correct
- It proves who spoke
- It guarantees transcription accuracy
- It is a model output, not necessarily a verified identity
- Why preserve the original recording and timestamps where permitted?
- They automatically correct word error rate
- They let reviewers check uncertain transcript passages against the source
- They replace the transcript reference
- They guarantee that no words were omitted
- Which error types does word error rate count?
- Speaker changes and audio duration
- Only punctuation and capitalization
- Substitutions, deletions, and insertions
- Translation and summarization errors
- Why report normalization rules with a WER score?
- Punctuation, casing, and tokenization choices affect the result
- Normalization verifies who spoke
- Rules remove all semantic errors
- It makes all datasets comparable automatically
- A system transcribes silence as confident words. What does the guide suggest testing?
- Speaker identity accuracy
- Only clean speech from one speaker
- Translation quality
- Silence and non-speech audio