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Quantum neural network in speech recognition

Fei Li, Shengmei Zhao, Baoyu Zheng

Published Oct 1, 2003• 36 citations
Publisher

Abstract

This paper describes a new kind of neural network - quantum neural network (QNN) and its application to recognition of continuous digits. QNN combines the advantages of neural modeling and fuzzy theoretic principles. Experiment results show that more than 15% error reduction is achieved on a speaker-independent continuous digits recognition task compared with the backpropagation (BP) network.

DOI
10.1109/icosp.2002.1180022

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This paper describes a new kind of neural network - quantum neural network (QNN) and its application to recognition of continuous digits. QNN combines the advantages of neural modeling and fuzzy theoretic principles. Experiment results show that more than 15% error reduction is achieved on a speaker-independent continuous digits recognition task compared with the backpropagation (BP) network.
Quantum Neural Network (QNN), a burgeoning new field which integrates quantum computation with classical neural network, can improve the inadequacies of artificial neural network. A model of quantum neuron and Quantum Back Propagation (BP) Neural Network based on quantum neuron are investigated. A novel approach toward speech enhancement which adopts Quantum BP Neural Network is proposed. The numerical simulation results show that the proposed speech enhancement scheme resulted in better performance than traditional spectral subtraction, Wiener filtering method and RBF neural network.
Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks (QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Back-propagation (BP) algorithm and particle swarm optimization (PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.
Speaker recognition is the technique to identify the identity of a person from statistical features obtained from speech signals. Many speaker recognition techniques have been designed and implemented so far to efficiently recognize the speaker. From the existing review, it is found that the existing speaker recognition techniques suffer from the over-fitting issues. Therefore, to overcome the over-fitting issue in this paper, we design, a novel ensemble-based quantum neural network. It selects one base model (i.e. expert) for each query, and concentrates on inductive bias reduction. A set of quantum neural networks are trained by considering different kinds of quantum features and are afterwards used to recognize the speaker. In the end, ensembling is used to combine these classification results. Extensive experiments have been carried out by considering the proposed technique and existing competitive machine learning-based speaker recognition techniques on speaker recognition data. It is observed that the proposed technique outperforms existing speaker recognition techniques in terms of accuracy and sensitivity by 1.371% and 1.291%, respectively.