Single Channel Speech Enhancement Using Outlier Detection
This work addresses speech distortion for applications like hearing aids or communication systems, but it is incremental as it builds on existing dictionary-based methods.
The paper tackled the problem of speech distortion in single-channel speech enhancement by proposing a dictionary-based method that treats noise estimation as an outlier detection problem, achieving significant noise reduction for non-stationary noises while preserving speech in challenging environments.
Distortion of the underlying speech is a common problem for single-channel speech enhancement algorithms, and hinders such methods from being used more extensively. A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed. Spectral patches of clean speech are sampled and clustered to train a dictionary. Given a noisy speech spectral patch, the best matching dictionary entry is selected and used to estimate the noise power at each time-frequency bin. The noise estimation step is formulated as an outlier detection problem, where the noise at each bin is assumed present only if it is an outlier to the corresponding bin of the best matching dictionary entry. This framework assigns higher priority in removing spectral elements that strongly deviate from a typical spoken unit stored in the trained dictionary. Even without the aid of a separate noise model, this method can achieve significant noise reduction for various non-stationary noises, while effectively preserving the underlying speech in more challenging noisy environments.