通过机器学习改进语音合成自然prosody的自然prosody
Joseph Kane1,2, Michael N Johnstone1,2, Patryk Szewczyk1,2
1Cyber Security Cooperative Research Centre, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
研究人员使用LSTM神经网络开发了一种新的机器学习方法,为计算机生成的语音添加现实的表达式. 这增强了人机交互和人工声音,使合成语音更自然.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 语音合成 语音合成
背景情况:
- 当前的人与计算机接口 (HCI) 努力实现无互动.
- 计算机生成的语音往往缺乏自然的旋律 (语调和节奏),听起来不像人类.
- 提高合成语音现实性对于电子阅读器和辅助说话设备等应用至关重要.
研究的目的:
- 通过结合类似人类的旋律和旋律元素来增强计算机生成的文本转化为语音 (TTS) 算法.
- 为了增加合成声音的现实性.
- 改进电子阅读应用程序和人工声音,以帮助需要语音辅助的人.
主要方法:
- 探索使用机器学习的新方法,特别是长短期记忆 (LSTM) 神经网络.
- 实现基于LSTM的编码器,为录制或生成的声音添加平行语言元素.
- 通过实验室实验部署了用于数字语音改进的原型模块化平台.
主要成果:
- 基于LSTM的编码器展示了产生现实的语音的能力.
- 实验室实验为开发的语音改进算法提供了令人鼓舞的结果.
- 研究了算法组合的优化和边缘情况下的性能.
结论:
- 这种基于LSTM的新方法显示了产生更自然,更现实的计算机生成语音的重大前景.
- 进一步的研究将集中在算法优化和对现有方法的比较性能分析上.
- 开发的技术在改进音频编解码器,恢复旧录音和增强HCI方面具有潜在的应用.
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