Real-Time Speech Enhancement Based on Convolutional Recurrent Neural Network  

作　　者：S.Girirajan A.Pandian 

机构地区：[1]Department of Computer Science and Engineering,School of Computing,SRM Institute of Science and Engineering,Kattankulathur,Tamil Nadu,India

出　　处：《Intelligent Automation & Soft Computing》2023年第2期1987-2001,共15页智能自动化与软计算（英文）

摘　　要：Speech enhancement is the task of taking a noisy speech input and pro-ducing an enhanced speech output.In recent years,the need for speech enhance-ment has been increased due to challenges that occurred in various applications such as hearing aids,Automatic Speech Recognition(ASR),and mobile speech communication systems.Most of the Speech Enhancement research work has been carried out for English,Chinese,and other European languages.Only a few research works involve speech enhancement in Indian regional Languages.In this paper,we propose a two-fold architecture to perform speech enhancement for Tamil speech signal based on convolutional recurrent neural network(CRN)that addresses the speech enhancement in a real-time single channel or track of sound created by the speaker.In thefirst stage mask based long short-term mem-ory(LSTM)is used for noise suppression along with loss function and in the sec-ond stage,Convolutional Encoder-Decoder(CED)is used for speech restoration.The proposed model is evaluated on various speaker and noisy environments like Babble noise,car noise,and white Gaussian noise.The proposed CRN model improves speech quality by 0.1 points when compared with the LSTM base model and also CRN requires fewer parameters for training.The performance of the pro-posed model is outstanding even in low Signal to Noise Ratio(SNR).

关 键 词：Speech enhancement convolutional encoder-decoder long short-term memory noise suppression speech restoration 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]