An efficient speaker recognition using quantum neural network
Abstract
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.
