@InProceedings{Ciba2023_1194,
author = {Stefan Ciba and Mohammed Krini and Amir Rajabi},
booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2023},
title = {Approach to Speaker-Generalized Spectral Envelope Estimation by Deep Recurrent Neural Network for Speech Reconstruction in a Speech Enhancement System},
year = {2023},
editor = {Christoph Draxler},
month = mar,
pages = {202--208},
publisher = {TUDpress, Dresden},
abstract = {Classical algorithms for Speech Enhancement (SE) often show unsatisfactory results in loud or exquisite noise scenarios which can be stationary or transient. Data-driven estimation techniques can outperform classical algorithms by means of machine learning. In this work machine learning and the classical approach are brought together in a Speech Enhancement System (SE-System). Furthermore, the source-filter model of speech production is used in such a way, that spectral speech features, namely excitation and envelope, can be estimated separately and subsequently combined. The estimation of the envelope was done by a Deep Recurrent Neural Network (Deep-RNN) as regressive model. The spectral envelope is extracted by an Infinite Impulse Response-Filter (IIR-Filter). The Deep-RNN is trained with many speakers and tested with several unseen speakers, to approach speaker generalization. Finally, the estimations as well as the potential of signal improvements, by applying the ideal excitation by the SE-System, are measured and discussed.},
isbn = {978-3-95908-303-4},
issn = {0940-6832},
keywords = {Poster},
url = {https://www.essv.de/pdf/pdf/2023_202_208.pdf},
}