A Comparative Analysis of Speech Enhancement Techniques Based on Sparsity Features

This paper presents a comparative analysis of speech enhancement algorithms based on speech sparsity features. The sparsity of a speech is a signal-specific characteristic that plays a crucial role in identifying speech signal components from noisy speech. Conventional enhancement techniques like Spectral subtraction, Sub-space and statistical methods require knowledge of noise distribution for better performance. The paper compares major sparsitybased enhancement by highlighting the merits and demerits by comparing quality and intelligibility. An objective evaluation of quality and intelligibility has been performed to show relative performance on a common database. Results show that dictionary-based speech enhancement aided with voice activity detection and spectral subtraction performs better than others. The performances improve further if noise-type and noise dictionary is known as apriori in the joint dictionary and nonnegative matrix factorization method. This analysis wm help the researcher understand sparsity-based enhancement techniques and develop techniques to overcome existing issues.