Abstract
It is shown that under certain conditions it is possible to obtain a
good speech estimate from noise without requiring noise estimation.
We study an implementation of the theory, namely wide matching,
for speech enhancement. The new approach performs sentence-wide
joint speech segment estimation subject to maximum recognizability
to gain noise robustness. Experiments have been conducted to evaluate
the new approach with variable noises and SNRs from -5 dB to
noise free. It is shown that the new approach, without any estimation
of the noise, significantly outperformed conventional methods in the
low SNR conditions while retaining comparable performance in the
high SNR conditions. It is further suggested that the wide matching
and deep learning approaches can be combined towards a highly
robust and accurate speech estimator.
Original language | English |
---|---|
Title of host publication | Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 5 |
DOIs | |
Publication status | Published - 19 May 2016 |
Event | The 41st IEEE International Conference on Acoustics, Speech and Signal Processing - Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016 |
Conference
Conference | The 41st IEEE International Conference on Acoustics, Speech and Signal Processing |
---|---|
Country | China |
City | Shanghai |
Period | 20/03/2016 → 25/03/2016 |