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Tabla de Contenidos: “…13.6.3 Updating the Environmental Parameters 360 -- 13.6.4 Implementation Details 360 -- 13.6.5 Experiments using NAT 361 -- 13.7 Discussion 364 -- 13.7.1 Comparison of Training Algorithms 364 -- 13.7.2 Comparison to Speaker Adaptive Training 364 -- 13.7.3 Related Adaptive Training Methods 365 -- 13.8 Conclusion 366 -- References 366 -- Part Five COMPENSATION FOR INFORMATION LOSS -- 14 Missing-Data Techniques: Recognition with Incomplete Spectrograms 371 / Jon Barker -- 14.1 Introduction 371 -- 14.2 Classification with Incomplete Data 373 -- 14.2.1 A Simple Missing Data Scenario 374 -- 14.2.2 Missing Data Theory 376 -- 14.2.3 Validity of the MAR Assumption 378 -- 14.2.4 Marginalising Acoustic Models 379 -- 14.3 Energetic Masking 381 -- 14.3.1 The Max Approximation 381 -- 14.3.2 Bounded Marginalisation 382 -- 14.3.3 Missing Data ASR in the Cepstral Domain 384 -- 14.3.4 Missing Data ASR with Dynamic Features 386 -- 14.4 Meta-Missing Data: Dealing with Mask Uncertainty 388 -- 14.4.1 Missing Data with Soft Masks 388 -- 14.4.2 Sub-band Combination Approaches 391 -- 14.4.3 Speech Fragment Decoding 393 -- 14.5 Some Perspectives on Performance 395 -- References 396 -- 15 Missing-Data Techniques: Feature Reconstruction 399 / Jort Florent Gemmeke, Ulpu Remes -- 15.1 Introduction 399 -- 15.2 Missing-Data Techniques 401 -- 15.3 Correlation-Based Imputation 402 -- 15.3.1 Fundamentals 402 -- 15.3.2 Implementation 404 -- 15.4 Cluster-Based Imputation 406 -- 15.4.1 Fundamentals 406 -- 15.4.2 Implementation 408 -- 15.4.3 Advances 409 -- 15.5 Class-Conditioned Imputation 411 -- 15.5.1 Fundamentals 411 -- 15.5.2 Implementation 412 -- 15.5.3 Advances 413 -- 15.6 Sparse Imputation 414 -- 15.6.1 Fundamentals 414 -- 15.6.2 Implementation 416 -- 15.6.3 Advances 418 -- 15.7 Other Feature-Reconstruction Methods 420 -- 15.7.1 Parametric Approaches 420 -- 15.7.2 Nonparametric Approaches 421 -- 15.8 Experimental Results 421 -- 15.8.1 Feature-Reconstruction Methods 422 -- 15.8.2 Comparison with Other Methods 424.…”