Huawei HCIP - AI EI Developer V2.5 Exam H13-321_V2.5 Question # 8 Topic 1 Discussion
H13-321_V2.5 Exam Topic 1 Question 8 Discussion:
Question #: 8
Topic #: 1
Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
MFCCs are a widely used feature extraction method in speech recognition. The process involves:
Converting the time-domain signal to the frequency domain using the Fourier transform.
Mapping the frequency scale to theMel scaleto mimic human hearing perception.
Taking the logarithm of the power spectrum to emphasize perceptually important differences.
Applying the discrete cosine transform (DCT) to obtaincepstral coefficients.
These steps capture the spectral envelope, which is important for distinguishing phonemes in speech.
Exact Extract from HCIP-AI EI Developer V2.5:
"MFCCs transform audio to the Mel scale, applying log compression and cepstral transformation to align with human auditory characteristics."
[Reference:HCIP-AI EI Developer V2.5 Official Study Guide – Chapter: Speech Feature Extraction, ]
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