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This paper considers the problem of rapid and robust speaker adaptation in Automatic Speech Recognition (ASR) systems. We propose an approach using combination of eigenspace-based maximum likelihood linear regression (EMLLR) and Minimum Classification Error (MCE). MCE is used to estimate the coefficients of eigenvoices as an alternative to maximum likelihood. Experimental results on TIMIT database illustrate that using MCE leads to up to 1.9% improvement in phoneme error rate. Moreover, it is shown that increasing the amounts of adaptation data has less effect in proposed method than conventional EMLLR. Furthermore, the results achieved using genetic algorithm (GA) to estimate eigenvoices coefficients are compared to proposed method using MCE. The adaptation results using MCE show better performance than GA. [ABSTRACT FROM AUTHOR]

In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN's performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN's accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field., (© 2021 Walter de Gruyter GmbH, Berlin/Boston.)