Prueba de detección de actividad de voz

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. ¿Cuál es el trabajo principal de la detección de actividad de voz?
   • Decidir si el audio contiene voz o no
   • Identificar quién está hablando
   • Traducir voz a texto
   • Generar voces sintéticas
2. 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
3. What does silence_duration_ms configure in server_vad?
   • The length of the speaker embedding
   • La frecuencia de muestreo de audio
   • How long a silent interval is used to detect that speech has stopped
   • How many words appear in a transcript
4. What range does the documented server_vad activation threshold use?
   • -100 to 100
   • 1 to 1000
   • Only the strings “low” and “high”
   • 0 a 1
5. 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
6. 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
7. What is the documented default equivalent of semantic_vad eagerness set to auto?
   • Cero
   • High
   • Medio
   • No threshold
8. 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