Copula Models for Multivariate Density Estimation, Classification and Robust Speech Recognition Embargo Deposited

Chapter 2 starts with an introduction to the copula model and dierent multivariate copula functions. Then, we propose two computationally simple approaches to built multivariate copula functions in Chapter 3. Finally, we evaluate these approaches in dierent density estimation and classication tasks. In chapter 4, we propose two simple methods to address the mismatch between training and testing conditions, which is a major bottleneck in everyday applications of ASR systems. First, we introduce a simple copula-based feature enhancement method independent of the ASR backend. Later in this chapter, we show that this enhancement can be further optimized by integrating it into the acoustic model. For evaluation, we use large vocabulary speech recognition tasks on Aurora 4 and Chime4. Chapter 5 gives the conclusions and future work.

We evaluate the performance of both of the proposed methods on several density estimation tasks from the UCI Repository as well as our corpus of Monkey vocalizations, recorded at the Oregon National Primate Research Center. We find that our methods represent the data consistently better than Gaussian mixture models with equivalent number of parameters. We also evaluate our proposed methods on building generative classifiers for a number of classification tasks from the UCI Repository. We find that these generative models perform as well or better than discriminative classifiers such as a Support Vector Machine (SVM).

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  • bayestehtashk.alriza.2018.pdf
  • 10.6083/3484zh604
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  • 2018
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  • Bayestehtashk, A. "Copula Models for Multivariate Density Estimation, Classification and Robust Speech Recognition" (2018). OHSU Digital Collections. bayestehtashk.alriza.2018.pdf
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  • 12/14/2021
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