A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition

TL;DR

This paper introduces a novel speech feature fusion approach using cross gate parallel CNNs to enhance speaker recognition by leveraging complementary information from multi-resolution Mel filter bank features.

Contribution

The paper proposes a new fusion method with a cross gate parallel CNN that effectively captures complementary features from multi-resolution Mel filter bank features for speaker recognition.

Findings

Outperforms existing state-of-the-art systems

Effective in capturing complementary features

Improves speaker recognition accuracy

Abstract

In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN). The Mel filter bank features (MFBFs) of different frequency resolutions can be extracted from each speech frame of a speaker's speech by several Mel filter banks, where the numbers of the triangular filters in the Mel filter banks are different. Due to the frequency resolutions of these MFBFs are different, there are some complementaries for these MFBFs. The CG-PCNN is utilized to extract the deep features from these MFBFs, which applies a cross gate mechanism to capture the complementaries for improving the performance of the speaker recognition system. Then, the fusion feature can be obtained by concatenating these deep features for speaker recognition. The experimental results show that the speaker recognition system with the proposed speech feature fusion method is effective, and marginally outperforms the existing state-of-the-art systems.