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음성 활동 감지 퀴즈
스트리밍 오디오의 음성 활동 감지, 프레임 수준 음성 결정, 숙취 동작 및 임계값 균형을 검토합니다.
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음성 활동 감지의 주요 업무는 무엇인가요?
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- 음성 활동 감지의 주요 업무는 무엇인가요?
- 오디오에 음성이 포함되어 있는지 여부 결정
- 말하는 사람이 누구인지 식별
- 음성을 텍스트로 번역
- 합성 음성 생성
- How does the OpenAI Realtime API’s server_vad mode determine audio chunks?
- By identifying the speaker’s legal name
- By using periods of silence
- By examining image frames
- By grouping words alphabetically
- What does silence_duration_ms configure in server_vad?
- The length of the speaker embedding
- 오디오 샘플링 속도
- How long a silent interval is used to detect that speech has stopped
- How many words appear in a transcript
- What range does the documented server_vad activation threshold use?
- -100 to 100
- 1 to 1000
- Only the strings “low” and “high”
- 0 대 1
- How does semantic_vad estimate that a user has finished speaking?
- It uses a semantic classifier based on the words the user has uttered
- It waits for exactly 500 milliseconds of silence only
- It checks for a camera image
- It counts speaker turns from a diarization transcript
- What does high eagerness do in semantic_vad?
- It waits longer before every response
- It chunks audio sooner and can return transcription events faster
- It disables all speech detection
- It increases the number of speakers in the audio
- What is the documented default equivalent of semantic_vad eagerness set to auto?
- 영
- High
- 중간
- No threshold
- Why may a streaming VAD include padding before detected speech?
- To add words that were not spoken
- To identify the real-world speaker name
- To reduce every utterance to one frame
- To preserve audio immediately preceding the detected onset