An Evaluation Study Of Speaker And Noise Adaptation For Nonnegative Matrix Factorization Based Speech Enhancement
In this study, we focus on evaluating the speech enhancement algorithm based on the nonnegative matrix factorization (NMF) technique under various distortion environments. The NMF spectral basis matrices for both speech and noise are obtained in a manner of supervised learning, and thus the performance of their associated NMF speech enhancement degrades as the speaker and/or noise characteristics are not matched for the learning and evaluation environment. An appropriate adaptation scheme helps to alleviate the aforementioned mismatch effect and leads to superior level of speech enhancement. The experiments conducted on a subset of the Aurora-2 connected digit database show that a linear adaptation for the speech/noise spectral matrices provides the NMF-based enhancement algorithms with significant improvement in elevating the speech quality under a wide range of signal-to-noise (SNR) environments.
Index Terms- Nonnegative Matrix Factorization, Speech Enhancement, Speaker Adaptation, Noise Adaptation