Comparative Study of Spectral Mapping Techniques for Enhancement of Throat Microphone Speech

The objective of this work is to study the suitability of existing spectral mapping methods for enhancement of throat microphone (TM) speech, and propose a more elegant method for spectral mapping. Gaussian mixture models (GMM) and neural networks (NN) have been used for spectral mapping. Though GMM-based mapping captures the variability among speech sounds through multiple mixtures, it can only provide a linear map between the source and the target. On the other hand, NN-based mapping is capable of providing a nonlinear map but a single mapping scheme may not handle variability across different speech sounds. Incorporating the advantages from these approaches, we propose a spectral mapping method using multiple neural networks. Speech data is clustered using k-means algorithm, and a separate neural network is employed to capture the mapping within each cluster. Objective evaluation has shown that proposed method is better than both GMM-base and NN-base mapping scheme