Identifying optimised speaker identification model using hybrid GRU-CNN feature extraction technique

Abstract

Extracting vigorous and discriminative features and selecting an appropriate classifier model to identify speakers from voice clips are challenging tasks. Thus, we considered signal processing techniques and deep neural networks for feature extraction along with state-of-art machine-learning models as classifiers. Also, we introduced a hybrid gated recurrent unit (GRU) and convolutional neural network (CNN) as a novel feature extractor for optimising the subspace loss to extract the best feature vector. Additionally, space-time is contemplated as a computational parameter for finding the optimal speaker identification pipeline. Later, we have inspected the pipeline in a large-scale VoxCeleb dataset comprising 6,000 real world speakers with multiple voices achieving GRU-CNN + R-CNN for the highest accuracy and F1-score as well as GRU-CNN + CNN for maximum precision and LPC + KNN for the highest recall. Also, LPCC + R-CNN and MFCC + R-CNN are accomplished as optimal in terms of memory usage and time respectively.