Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: VC-ENHANCE: Speech Restoration with Integrated Noise Suppression and Voice Conversion
summary: Noise suppression (NS) algorithms are effective in improving speech quality
in many cases. However, aggressive noise suppression can damage the target
speech, reducing both speech intelligibility and quality despite removing the
noise. This study proposes an explicit speech restoration method using a voice
conversion (VC) technique for restoration after noise suppression. We observed
that high-quality speech can be restored through a diffusion-based voice
conversion stage, conditioned on the target speaker embedding and speech
content information extracted from the de-noised speech. This speech
restoration can achieve enhancement effects such as bandwidth extension,
de-reverberation, and in-painting. Our experimental results demonstrate that
this two-stage NS+VC framework outperforms single-stage enhancement models in
terms of output speech quality, as measured by objective metrics, while scoring
slightly lower in speech intelligibility. To further improve the
intelligibility of the combined system, we propose a content encoder adaptation
method for robust content extraction in noisy conditions.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: VC-ENHANCE: Speech Restoration with Integrated Noise Suppression and Voice Conversion
summary: Noise suppression (NS) algorithms are effective in improving speech quality in many cases. However, aggressive noise suppression can damage the target speech, reducing both speech intelligibility and quality despite removing the noise. This study proposes an explicit speech restoration method using a voice conversion (VC) technique for restoration after noise suppression. We observed that high-quality speech can be restored through a diffusion-based voice conversion stage, conditioned on the target speaker embedding and speech content information extracted from the de-noised speech. This speech restoration can achieve enhancement effects such as bandwidth extension, de-reverberation, and in-painting. Our experimental results demonstrate that this two-stage NS+VC framework outperforms single-stage enhancement models in terms of output speech quality, as measured by objective metrics, while scoring slightly lower in speech intelligibility. To further improve the intelligibility of the combined system, we propose a content encoder adaptation method for robust content extraction in noisy conditions.
id: http://arxiv.org/abs/2409.06126v1
judge
Write [vclab::confirmed] or [vclab::excluded] in comment.