使用跨域特征的普通话电子喉语音转换
Mandarin Electrolaryngeal Speech Voice Conversion using Cross-domain Features
AI总结：
针对全喉切除患者使用的电子喉语音质量与可懂度差的问题，本文提出融合频谱特征与自监督学习嵌入等跨域特征的新型电子喉语音转换系统，实验证明其性能优于仅用传统频谱特征的方案。
摘要：
因喉癌切除包括声带在内的全喉的患者可能会遇到说话困难。在这种情况下，通常会使用电子喉设备来产生语音，这类语音通常被称为电子喉语音（EL speech）。然而，EL语音的质量和可懂度较差。为解决这一问题，电子喉语音转换（ELVC）是一种用于提升EL语音可懂度与质量的方法。本文提出了一种融合跨域特征的新型ELVC系统，具体采用了频谱特征和自监督学习（SSL）嵌入。实验结果表明，与仅使用传统频谱特征相比，应用跨域特征能显著提升ELVC任务的转换性能。
英文摘要：
Patients who have had their entire larynx removed, including the vocal folds, owing to throat cancer may experience difficulties in speaking. In such cases, electrolarynx devices are often prescribed to produce speech, which is commonly referred to as electrolaryngeal speech (EL speech). However, the quality and intelligibility of EL speech are poor. To address this problem, EL voice conversion (ELVC) is a method used to improve the intelligibility and quality of EL speech. In this paper, we propose a novel ELVC system that incorporates cross-domain features, specifically spectral features and self-supervised learning (SSL) embeddings. The experimental results show that applying cross-domain features can notably improve the conversion performance for the ELVC task compared with utilizing only traditional spectral features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
