2022/12/01 by Peter Juma Ochieng, Ochieng, Peter · 7 citations
Computer Science · #Artificial intelligence #Artificial neural network #Audio and Speech Processing (eess.AS) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Monaural #Noise reduction #Pipeline (software) #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #Speech enhancement #Speech processing #Speech recognition #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.00369
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/12/01 · openalex created_date 2022/12/13 · openalex updated_date 2026/08/08
Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in these domains in task such as machine translation and image generation. Due to their success, these data driven techniques have been applied in audio domain. More specifically, DNN models have been applied in speech enhancement domain to achieve denosing, dereverberation and multi-speaker separation in monaural speech enhancement. In this paper, we review some dominant DNN techniques being employed to achieve speech separation. The review looks at the whole pipeline of speech enhancement from feature extraction, how DNN based tools are modelling both global and local features of speech and model training (supervised and unsupervised). We also review the use of speech-enhancement pre-trained models to boost speech enhancement process. The review is geared towards covering the dominant trends with regards to DNN application in speech enhancement in speech obtained via a single speaker.