An Enhanced Deep Neural Network-Based Approach for Speaker Recognition Using Triumvirate Euphemism Strategy

Abstract

Automatic Speech Recognition (ASR) has been an intensive research area during the recent years in internet to enable natural human–machine communication. However, the existing Deep Neutral Network (DNN) techniques need more focus on feature extraction process and recognition accuracy. Thus, an enhanced deep neural network (DNN)-based approach for speaker recognition with a novel Triumvirate Euphemism Strategy (TES) is proposed. This overcomes poor feature extraction from Mel-Frequency Cepstral Coefficient (MFCC) map by extracting the features based on petite, hefty and artistry of the features. Then, the features are trained with Silhouette Martyrs Method (SMM) without any inter-class and intra-class separability problems and margins are affixed between classes with three new loss functions, namely A-Loss, AM-Loss and AAM-Loss. Additionally, the parallelization is done by a mini-batch-based BP algorithm in DNN. A novel Frenzied Heap Atrophy (FHA) with a multi-GPU model is introduced in addition with DNN to enhance the parallelized computing that accelerates the training procedures. Thus, the outcome of the proposed technique is highly efficient that provides feasible extraction features and gives incredibly precise results with 97.5% accuracy in the recognition of speakers. Moreover, various parameters were discussed to prove the efficiency of the system and also the proposed method outperformed the existing methods in all aspects.