Unsupervised Speech Enhancement Based On Multichannel Nmf-informed Beamforming For Noise-robust Automatic Speech Recognition

Abstract

This paper describes multichannel speech enhancement for improving automatic speech recognition (ASR) in noisy environments. Recently, the minimum variance distortionless response (MVDR) beamforming has widely been used because it works well if the steering vector of speech and the spatial covariance matrix (SCM) of noise are given. To estimating such spatial information, conventional studies take a supervised approach that classifies each time-frequency (TF) bin into noise or speech by training a deep neural network (DNN). The performance of ASR, however, is degraded in an unknown noisy environment. To solve this problem, we take an unsupervised approach that decomposes each TF bin into the sum of speech and noise by using multichannel nonnegative matrix factorization (MNMF). This enables us to accurately estimate the SCMs of speech and noise not from observed noisy mixtures but from separated speech and noise components. In this paper we propose online MVDR beamforming by effectively

Stats

• citations57
• S2 citations—
• github stars0
• HF likes0
• heat score13.23
• arxiv keyshimada2019unsupervised

Related papers

• Dnn-free Low-latency Adaptive Speech Enhancement Based On Frame-online Beamforming Powered By Block-online Fastmnmf (2022)0.00
• ADL-MVDR: All Deep Learning MVDR Beamformer For Target Speech Separation (2020)15.00
• Run-time Adaptation Of Neural Beamforming For Robust Speech Dereverberation And Denoising (2024)0.00
• Supervised And Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization (2017)18.80
• Multi-talker MVDR Beamforming Based On Extended Complex Gaussian Mixture Model (2019)0.00
• Noise Robust Speech Recognition Using Multi-channel Based Channel Selection And Channelweighting (2016)0.00
• Semi-supervised Multichannel Speech Enhancement With Variational Autoencoders And Non-negative Matrix Factorization (2018)12.25
• Multichannel Loss Function For Supervised Speech Source Separation By Mask-based Beamforming (2019)7.50