Cuestionario vinculado a una guía · Medio Nivel

Prueba de análisis de voz de IA en call center

Pruebe el reconocimiento de voz, la diarioización y el análisis de sentimientos en investigaciones de centros de llamadas, incluidos los límites y el alcance de cada estudio citado.

Rutas de guía relacionadasIA en análisis de voz de call center
Pregunta 1 de 8

What output can automatic speech recognition provide for speech analytics?

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

  1. What output can automatic speech recognition provide for speech analytics?
    • A machine-generated transcript for downstream text analysis
    • A guaranteed speaker identity
    • A final diagnosis of the caller’s emotional state
    • A payment authorization
  2. What does speaker diarization do in a call recording?
    • Traduce la llamada a otro idioma.
    • Segments the recording into speaker turns and assigns speaker labels
    • Mide el volumen de la llamada
    • Cifra la grabación
  3. In IBM’s two-stream call-center emotion-recognition study, which information streams are combined?
    • Video and keystroke timing
    • Customer purchase history and account balance
    • Acoustic and lexical information
    • Only transcript punctuation
  4. ¿Qué característica acústica ayuda a detectar el estado emocional de un cliente más allá de las palabras utilizadas?
    • Tamaño de archivo de la grabación
    • La identificación del empleado del agente.
    • El operador telefónico
    • Tono, ritmo y volumen (prosodia)
  5. In a two-stream call-center emotion system, what do acoustic features contribute?
    • Evidence from vocal delivery, such as how speech sounds
    • The caller’s verified identity
    • The account’s payment history
    • A guaranteed diagnosis of the caller’s emotion
  6. What task did Vidrascu and Devillers study for customer-service phone calls?
    • Predicting credit scores from call recordings
    • Speaker segmentation and emotion-related analysis of conversational turns
    • Generating live agent sales recommendations
    • Automating legal decisions about customer accounts
  7. Why might a call-center sentiment system combine lexical and acoustic features?
    • Acoustic features replace the transcript in every task
    • Lexical features reveal the phone carrier
    • The words and the way they are spoken provide complementary signals
    • Combining them guarantees the caller’s true emotion is known
  8. What does word-error rate compare in speech recognition evaluation?
    • Speaker identity against a customer account
    • Emotion labels against call duration
    • Call volume against agent schedule
    • Substitutions, deletions and insertions against the words in a reference transcript