Research map / Learning theory and algorithms / Sample complexity
Quantum Expectation-Maximization for Gaussian mixture models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add noise to the GMM at each training iteration (δθ=0.038, δμ=0.5) | GMM speaker-recognition model · VoxForge speaker recognition | accuracy: 98.7% vs 97.1% | Helped |
| Threshold small covariance singular values and add noise to the GMM | ML estimate of GMM · VoxForge speaker recognition | accuracy: 98.7% vs 97.1% | Helped |
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