Guide-yakabatana mibvunzo · Pakati Nhanho

AI muCall Center Speech Analytics Quiz

Edza kucherechedzwa kwekutaura, diarization, uye ongororo yemanzwiro mukufona-center tsvagiridzo, kusanganisira miganhu uye chiyero chechidzidzo chega chega chakanyorwa.

Nzira dzekutungamira dzinoenderanaAI MuCall Center Speech Analytics
Mubvunzo 1 ye 8

What output can automatic speech recognition provide for speech analytics?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  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?
    • Inoshandura kudanwa kune mumwe mutauro
    • Segments the recording into speaker turns and assigns speaker labels
    • Inopima kuti runhare rwaive neruzha rwakadii
    • Encrypts kurekodha
  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. Ndeipi acoustic ficha inobatsira kuona manzwiro emutengi kupfuura mazwi anoshandiswa?
    • Saizi yefaira yekurekodha
    • ID yemushandi wemumiririri
    • Mutakuri wefoni
    • Pitch, kumhanya, uye vhoriyamu (prosody)
  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