ROBUST SPEECH RECOGNITION USING MULTIVARIATE COPULA MODELS

In this paper, we continue our investigation into copula models for real-valued multivariate features with the goal of compensating for the mismatch in the training and the testing conditions. Previously, we reported results on UCI classification tasks where our method consistently outperformed other competing classifiers [1]. Here, we extend this work from classification to recognition and elaborate further on the mathematical properties of our models in the form of lemmas. We report results on the Aurora 4 automatic speech recognition (ASR) task which contains utterances with wide range of background noise that are not well represented in the training data. Our results show that the proposed copula-based models improve the accuracy by about 7% (11.6 vs 12.4) over a comparable baseline.