Quantum-Inspired Evolutionary Algorithms for Neural Network Weight Distribution: A Classification Model for Parkinson\u27s Disease

Parkinson’s Disease is a degenerative neurological disorder with unknown origins, making it impossible to be cured or even diagnosed. The following article presents a Three-Layered Perceptron Neural Network model that is trained using a variety of evolutionary as well as quantum-inspired evolutionary algorithms for the classification of Parkinson\u27s Disease. Optimization algorithms such as Particle Swarm Optimization, Artificial Bee Colony Algorithm and Bat Algorithm are studied along with their quantum-inspired counter-parts in order to identify the best suited algorithm for Neural Network Weight Distribution. The results show that the quantum-inspired evolutionary algorithms perform better under the given circumstances, with qABC offering the highest accuracy of about 92.3%. The presented model can be used not only for disease diagnosis but is also likely to find its applications in various other fields as well

Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition

Denoising autoencoders (DAs) have shown success in gener-ating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltz-mann machines (RBMs) in an unsupervised fashion. Then it is unrolled to autoencoders, and fine-tuned by correspond-ing clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recog-nition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25 % absolute im-provement on the recognition accuracy under various SNRs. Index Terms — robust speech recognition, feature denois-ing, denoising autoencoder, deep neural network 1

Noise robust continuous digit recognition with reservoir-based acoustic models

Notwithstanding the many years of research, more work is needed to create automatic speech recognition (ASR) systems with a close-to-human robustness against confounding factors such as ambient noise, channel distortion, etc. Whilst most work thus far focused on the improvement of ASR systems embedding Gaussian Mixture Models (GMM)s to compute the acoustic likelihoods in the states of a Hidden Markov Model (HMM), the present work focuses on the noise robustness of systems employing Reservoir Computing (RC) as an alternative acoustic modeling technique. Previous work already demonstrated good noise robustness for continuous digit recognition (CDR). The present paper investigates whether further progress can be achieved by driving reservoirs with noise-robust inputs that have been shown to raise the robustness of GMM-based systems, by introducing bi-directional reservoirs and by combining reservoirs with GMMs in a single system. Experiments on Aurora-2 demonstrate that it is indeed possible to raise the noise robustness without significantly increasing the system complexity

COMPARING MULTILAYER PERCEPTRON TO DEEP BELIEF NETWORK TANDEM FEATURES FOR ROBUST ASR

In this paper, we extend the work done on integrating multilayer perceptron (MLP) networks with HMM systems via the Tandem approach. In particular, we explore whether the use of Deep Belief Networks (DBN) adds any substantial gain over MLPs on the Aurora2 speech recognition task under mismatched noise conditions. Our findings suggest that DBNs outperform single layer MLPs under the clean condition, but the gains diminish as the noise level is increased. Furthermore, using MFCCs in conjunction with the posteriors from DBNs outperforms merely using single DBNs in low to moderate noise conditions. MFCCs, however, do not help for the high noise settings