Summary
Objective
Speech enhancement has become a promising technique to accommodate demands of the
improvement in quality of a degraded speech signal. The main works now focus on separating
normal speech from noise, but have neglected the low quality of impaired speech influenced
by anomalous glottis flow. In order to effectively enhance the pathological speech,
it is essential to design a separation mechanism for extracting high-dimensional timbre
features and speech features separately to suppress low-dimensional noises.
Methods
In this paper, we propose an enhancement model GBNF-VAE to extract timbre efficiently
by reducing anomalous airflow noise interference, and by combining the semantic features
with timbre features to synthesize the enhanced speech. In particular, the bottleneck
feature can characterize the timbre by the controlled number of nodes through the
Golden Section method, which effectively improves computational efficiency. In addition,
variational autoencoder is adopted to extract semantic features which are combined
with the previous timbre features to synthesize the enhanced speech.
Results
Finally, spectrum observation, objective indicators and subjective evaluation all
show the outstanding performance of GBNF-VAE in pathological speech quality enhancement.
Key Words
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Article info
Publication history
Published online: May 09, 2023
Accepted:
March 10,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
