Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Ìdánwò Idanimọ imolara Ọrọ

Checks acoustic cues, dataset limits, cross-corpus generalization, label ambiguity, and the narrow EU workplace and education prohibition.

Jẹmọ itọsọna awọn ọnaIfarahan Ọrọ idanimọ
Ibeere 1 ti 8

Kini Idanimọ ẹdun Ọrọ ni akọkọ ṣe itupalẹ?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini Idanimọ ẹdun Ọrọ ni akọkọ ṣe itupalẹ?
    • Awọn ẹya akositiki ti ohun bii ipolowo ati agbara
    • Awọn ọrọ gangan ninu iwe afọwọkọ kan
    • Giramu ti gbolohun naa
    • Awọn oju ti agbọrọsọ
  2. Which acoustic properties can speech-emotion models use as cues?
    • Only the spelling of transcribed words
    • Pitch, energy, speaking rate, and voice-quality features
    • A person’s job title
    • The number of speakers in a company
  3. Kini idi ti isamisi data ẹdun paapaa nira paapaa?
    • Awọn faili ohun ti tobi ju lati fipamọ
    • Awọn gbohungbohun ko le ṣe igbasilẹ imolara
    • Imolara jẹ ẹya-ara ati awọn alatumọ eniyan nigbagbogbo ko gba
    • Awọn ẹdun ko han ninu ọrọ
  4. Ewo ninu iwọnyi jẹ data-ọrọ ọrọ ẹdun ti a mọ daradara?
    • Pipa Net
    • COCO
    • SQuAD
    • IEMOCAP
  5. Bawo ni awọn eto SER ode oni ṣe nigbagbogbo n lo data aami to lopin?
    • Nipa ṣiṣe atunṣe awọn awoṣe abojuto ti ara ẹni bii wav2vec 2.0
    • Nipa yago fun awọn nẹtiwọki nkankikan patapata
    • Nipa lilo awọn ofin afọwọkọ nikan
    • Nipa gbigbasilẹ awọn miliọnu awọn agekuru ẹdun tuntun ni akọkọ
  6. What does the EU AI Act prohibit regarding emotion recognition?
    • All emotion-related research in any setting
    • Inferring emotions in workplace or educational settings, except for medical or safety reasons
    • Every voice assistant that detects speech
    • Any system that transcribes spoken words
  7. Why can a speech-emotion model trained on one dataset fail on another?
    • Emotion labels are identical across every corpus
    • Microphone and speaker differences never affect audio
    • Speakers, language, recording conditions, elicitation, and label distributions can differ
    • A trained model cannot process new recordings
  8. What is one limitation of treating a speech-emotion prediction as a person’s true internal state?
    • Acoustic features contain no information
    • A predicted label is a clinical diagnosis by default
    • The same voice always expresses the same emotion
    • A model infers patterns from audio and may not reflect the speaker’s context or self-report