A Self-Supervised Convolutional Neural Network Approach for Speech Enhancement

Abstract

Enhancement of speech means modification to the speech which is degraded by noise. Speech enhancement leads to improvement in the intelligibility of speech to human listeners. Deep learning techniques have drawn tremendous attention for speech enhancement in recent years which require clean speech along with noisy speech for training purpose. However, availability of clean speech signal in naturalistic scenarios is challenging. To ameliorate it, this study proposes a deep neural network- based on speech enhancement approach without the requirement of clean speech to train the model called self-supervised learning. In the proposed framework, two CNN-based speech enhancement models have been deployed for two noisy conditions (babble noise and machinery noise). This work has been accomplished on two different datasets: IEEE speech corpus distorted with real-time noise and recorded speech signals in naturalistic environment. Experimental result demonstrates that the proposed framework achieved significant improvement in both subjective and objective measures.