A robust speaker identification system using the responses from a model of the auditory periphery

Engineering

• Speaker Identification 100%
• Speech Signal 50%
• Signal-to-Noise Ratio 25%
• Gaussians 25%
• Error Rate 25%
• Classification Accuracy 25%
• Gaussian Mixture Model 25%
• Frequency Domain 25%
• Speech Processing 25%
• Auditory Nerve 25%
• Noisy Environment 25%
• Characteristic Frequency 25%
• Frequency Tone 25%

Keyphrases

• Robust Speaker Identification 100%
• Identity Model 33%
• Frequency Cepstral Coefficients 33%
• New Speakers 33%
• Gaussian Mixture Model-universal Background Model (GMM-UBM) 33%
• Tone Frequency 33%

Computer Science

• Background Model 25%
• Identity Model 25%