在噪音中评估合成语音的可理解性
Ye Yang1, Dathan Nguyen1, Katherine Chen1
1Department of Biomedical Engineering, University of California, Irvine, California 92697, USA.
JASA express letters
|April 17, 2025
概括
合成的声音,就像人类的语言一样,在杂的环境下也可以被理解. 一些机器生成的声音甚至超过了人类的语音可理解性,并且可以更好地被自动语音识别系统识别.
科学领域:
- 语音处理 语音处理
- 人与计算机的互动.
- 声学语音学的声音学.
背景情况:
- 人类适应语音,以提高噪音的清晰度.
- 先进的语音合成技术使机器能够产生可理解的语音.
- 在噪音中评估合成语音的可理解性对于人机交互至关重要.
研究的目的:
- 评估合成语音在语音形噪音中的主观和客观可理解性.
- 来自主要平台的合成声音与人类言语的可理解性进行比较.
- 为了评估自动语音识别系统与合成语音的性能.
主要方法:
- 涉及人类听众的主观可理解性测试.
- 客观的理解度测量. 客观的理解度测量.
- 从三个主要语音合成平台对合成语音的评估.
- 使用语音形状噪声进行测试.
主要成果:
- 合成的声音表现出与人类声音相比的可理解范围.
- 一些合成的声音表现出比人类声音更高的可理解性.
- 两种现代自动语音识别系统,与人类听众相比,实现了10%更高的单词识别精度.
结论:
- 语音合成技术可以在杂的环境中产生高度可理解的声音.
- 合成的声音显示出与人类语音可理解性相匹配或超过的潜力.
- 自动语音识别系统在可理解的合成语音上表现出色.
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